EasyVisa Project

Context:

Business communities in the United States are facing high demand for human resources, but one of the constant challenges is identifying and attracting the right talent, which is perhaps the most important element in remaining competitive. Companies in the United States look for hard-working, talented, and qualified individuals both locally as well as abroad.

The Immigration and Nationality Act (INA) of the US permits foreign workers to come to the United States to work on either a temporary or permanent basis. The act also protects US workers against adverse impacts on their wages or working conditions by ensuring US employers' compliance with statutory requirements when they hire foreign workers to fill workforce shortages. The immigration programs are administered by the Office of Foreign Labor Certification (OFLC).

OFLC processes job certification applications for employers seeking to bring foreign workers into the United States and grants certifications in those cases where employers can demonstrate that there are not sufficient US workers available to perform the work at wages that meet or exceed the wage paid for the occupation in the area of intended employment.

Objective:

In FY 2016, the OFLC processed 775,979 employer applications for 1,699,957 positions for temporary and permanent labor certifications. This was a nine percent increase in the overall number of processed applications from the previous year. The process of reviewing every case is becoming a tedious task as the number of applicants is increasing every year.

The increasing number of applicants every year calls for a Machine Learning based solution that can help in shortlisting the candidates having higher chances of VISA approval. OFLC has hired your firm EasyVisa for data-driven solutions. You as a data scientist have to analyze the data provided and, with the help of a classification model:

• Facilitate the process of visa approvals.
• Recommend a suitable profile for the applicants for whom the visa should be certified or denied based on the drivers that significantly influence the case status.

Data Description

The data contains the different attributes of the employee and the employer. The detailed data dictionary is given below.

• case_id: ID of each visa application
• continent: Information of continent the employee
• education_of_employee: Information of education of the employee
• has_job_experience: Does the employee has any job experience? Y= Yes; N = No
• requires_job_training: Does the employee require any job training? Y = Yes; N = No
• no_of_employees: Number of employees in the employer's company
• yr_of_estab: Year in which the employer's company was established
• region_of_employment: Information of foreign worker's intended region of employment in the US.
• prevailing_wage: Average wage paid to similarly employed workers in a specific occupation in the area of intended employment. The purpose of the prevailing wage is to ensure that the foreign worker is not underpaid compared to other workers offering the same or similar service in the same area of employment.
• unit_of_wage: Unit of prevailing wage. Values include Hourly, Weekly, Monthly, and Yearly.
• full_time_position: Is the position of work full-time? Y = Full Time Position; N = Part Time Position
• case_status: Flag indicating if the Visa was certified or denied

Importing necessary libraries and data

# Installing the libraries with the specified version.
#!pip install numpy==1.25.2 pandas==1.5.3 scikit-learn==1.2.2 matplotlib==3.7.1 seaborn==0.13.1 xgboost==2.0.3

import warnings
warnings.filterwarnings("ignore")
# Libraries to help with reading and manipulating data
import numpy as np
import pandas as pd

# libaries to help with data visualization
import seaborn as sns
import matplotlib.pyplot as plt

# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)
# setting the precision of floating numbers to 5 decimal points
pd.set_option("display.float_format", lambda x: "%.5f" % x)

#Import libraries to build Ensemble models
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import BaggingRegressor,RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor, StackingRegressor
from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier, StackingClassifier, RandomForestClassifier, BaggingClassifier, StackingClassifier
from xgboost import XGBRegressor, XGBClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
from sklearn import metrics
from sklearn.model_selection import GridSearchCV, train_test_split
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score, roc_auc_score

import scipy.stats as stats

Note: After running the above cell, kindly restart the notebook kernel and run all cells sequentially from the start again.

df_main = pd.read_csv("EasyVisa.csv") #Loading dataset

# copying data to another variable to avoid any changes to original data
data = df_main.copy()

Data Overview

• Observations
• Sanity checks

View the first and last 5 rows of the data set.

data.head()

	case_id	continent	education_of_employee	has_job_experience	requires_job_training	no_of_employees	yr_of_estab	region_of_employment	prevailing_wage	unit_of_wage	full_time_position	case_status
0	EZYV01	Asia	High School	N	N	14513	2007	West	592.20290	Hour	Y	Denied
1	EZYV02	Asia	Master's	Y	N	2412	2002	Northeast	83425.65000	Year	Y	Certified
2	EZYV03	Asia	Bachelor's	N	Y	44444	2008	West	122996.86000	Year	Y	Denied
3	EZYV04	Asia	Bachelor's	N	N	98	1897	West	83434.03000	Year	Y	Denied
4	EZYV05	Africa	Master's	Y	N	1082	2005	South	149907.39000	Year	Y	Certified

Understanding the shape of the data set.

data.shape

(25480, 12)

• There are 25480 rows and 12 columns in the original dataset.

Checkng the unique values in each column.

# Dictionary to store unique values for each feature
unique_values = {}

# Loop through each column in the DataFrame
for column in data.columns:
    # Get unique values for the current column
    unique_values[column] = set(data[column].unique())

# Print unique values for each feature
for feature, values in unique_values.items():
    print(f"Unique values for {feature}: {values}")

Unique values for case_id: {'EZYV13000', 'EZYV13951', 'EZYV15453', 'EZYV4850', 'EZYV15206', 'EZYV6106', 'EZYV3599', 'EZYV351', 'EZYV13029', 'EZYV7011', 'EZYV23575', 'EZYV900', 'EZYV4706', 'EZYV16630', 'EZYV1900', 'EZYV6516', 'EZYV13297', 'EZYV24744', 'EZYV9935', 'EZYV11204', 'EZYV18289', 'EZYV17223', 'EZYV10200', 'EZYV968', 'EZYV10567', 'EZYV9668', 'EZYV3043', 'EZYV9596', 'EZYV18193', 'EZYV12632', 'EZYV21782', 'EZYV7014', 'EZYV17225', 'EZYV17231', 'EZYV2588', 'EZYV17919', 'EZYV9108', 'EZYV3672', 'EZYV19316', 'EZYV4431', 'EZYV8715', 'EZYV5772', 'EZYV21416', 'EZYV23189', 'EZYV1207', 'EZYV5336', 'EZYV3404', 'EZYV8366', 'EZYV2199', 'EZYV11976', 'EZYV6667', 'EZYV11155', 'EZYV15615', 'EZYV20512', 'EZYV8103', 'EZYV1623', 'EZYV22139', 'EZYV16299', 'EZYV23183', 'EZYV10197', 'EZYV14251', 'EZYV3414', 'EZYV2729', 'EZYV5429', 'EZYV21058', 'EZYV3859', 'EZYV12491', 'EZYV2867', 'EZYV25315', 'EZYV8608', 'EZYV12940', 'EZYV10384', 'EZYV1546', 'EZYV22991', 'EZYV497', 'EZYV20767', 'EZYV5795', 'EZYV13478', 'EZYV17889', 'EZYV19023', 'EZYV17015', 'EZYV1388', 'EZYV3619', 'EZYV542', 'EZYV19285', 'EZYV11042', 'EZYV16007', 'EZYV14352', 'EZYV12321', 'EZYV8114', 'EZYV6296', 'EZYV489', 'EZYV730', 'EZYV12753', 'EZYV12634', 'EZYV3323', 'EZYV7736', 'EZYV17598', 'EZYV4555', 'EZYV4339', 'EZYV2088', 'EZYV12917', 'EZYV18967', 'EZYV12824', 'EZYV15457', 'EZYV17105', 'EZYV19138', 'EZYV14645', 'EZYV6197', 'EZYV24340', 'EZYV9362', 'EZYV15604', 'EZYV21806', 'EZYV11685', 'EZYV11682', 'EZYV23110', 'EZYV15285', 'EZYV20252', 'EZYV120', 'EZYV15276', 'EZYV9370', 'EZYV12972', 'EZYV16791', 'EZYV24751', 'EZYV25456', 'EZYV16397', 'EZYV22690', 'EZYV2981', 'EZYV15200', 'EZYV16694', 'EZYV6463', 'EZYV4281', 'EZYV18409', 'EZYV13440', 'EZYV25056', 'EZYV21045', 'EZYV24115', 'EZYV18141', 'EZYV3973', 'EZYV12767', 'EZYV3349', 'EZYV13609', 'EZYV15902', 'EZYV14010', 'EZYV829', 'EZYV19210', 'EZYV1401', 'EZYV18454', 'EZYV10048', 'EZYV18999', 'EZYV21509', 'EZYV8261', 'EZYV12280', 'EZYV10333', 'EZYV4578', 'EZYV13573', 'EZYV13818', 'EZYV18792', 'EZYV25302', 'EZYV3083', 'EZYV16015', 'EZYV21213', 'EZYV8847', 'EZYV3186', 'EZYV4481', 'EZYV22736', 'EZYV24365', 'EZYV11965', 'EZYV13890', 'EZYV22823', 'EZYV6469', 'EZYV937', 'EZYV15049', 'EZYV16160', 'EZYV11177', 'EZYV11780', 'EZYV18780', 'EZYV11583', 'EZYV2768', 'EZYV7256', 'EZYV19220', 'EZYV4110', 'EZYV7661', 'EZYV12431', 'EZYV15060', 'EZYV14312', 'EZYV21008', 'EZYV16306', 'EZYV23965', 'EZYV7768', 'EZYV1036', 'EZYV13006', 'EZYV22577', 'EZYV21236', 'EZYV19703', 'EZYV14440', 'EZYV1531', 'EZYV24164', 'EZYV24107', 'EZYV16625', 'EZYV23570', 'EZYV6655', 'EZYV16585', 'EZYV13732', 'EZYV4259', 'EZYV10392', 'EZYV5001', 'EZYV7763', 'EZYV1210', 'EZYV17693', 'EZYV12067', 'EZYV5042', 'EZYV5499', 'EZYV18448', 'EZYV10922', 'EZYV13467', 'EZYV10439', 'EZYV4264', 'EZYV18771', 'EZYV23795', 'EZYV17040', 'EZYV19029', 'EZYV4316', 'EZYV8332', 'EZYV14796', 'EZYV10951', 'EZYV22629', 'EZYV19841', 'EZYV1104', 'EZYV19065', 'EZYV19617', 'EZYV21760', 'EZYV22622', 'EZYV2769', 'EZYV17907', 'EZYV19826', 'EZYV4982', 'EZYV25093', 'EZYV20219', 'EZYV13157', 'EZYV5974', 'EZYV3977', 'EZYV20106', 'EZYV2523', 'EZYV23974', 'EZYV21352', 'EZYV20192', 'EZYV10159', 'EZYV8240', 'EZYV14837', 'EZYV5636', 'EZYV16765', 'EZYV10381', 'EZYV25353', 'EZYV2160', 'EZYV17447', 'EZYV17592', 'EZYV6200', 'EZYV23686', 'EZYV5086', 'EZYV18793', 'EZYV10814', 'EZYV18911', 'EZYV4176', 'EZYV8878', 'EZYV1874', 'EZYV8080', 'EZYV11527', 'EZYV16526', 'EZYV25477', 'EZYV4564', 'EZYV17732', 'EZYV1738', 'EZYV13889', 'EZYV8268', 'EZYV12059', 'EZYV12482', 'EZYV24750', 'EZYV21801', 'EZYV8577', 'EZYV23917', 'EZYV8132', 'EZYV21179', 'EZYV5761', 'EZYV16855', 'EZYV19153', 'EZYV3911', 'EZYV16788', 'EZYV9177', 'EZYV3521', 'EZYV12230', 'EZYV17300', 'EZYV13239', 'EZYV19109', 'EZYV836', 'EZYV7744', 'EZYV4941', 'EZYV14125', 'EZYV22606', 'EZYV13422', 'EZYV19908', 'EZYV16748', 'EZYV20007', 'EZYV7869', 'EZYV19602', 'EZYV9768', 'EZYV8168', 'EZYV4434', 'EZYV1896', 'EZYV8721', 'EZYV2892', 'EZYV18824', 'EZYV1827', 'EZYV6099', 'EZYV720', 'EZYV12493', 'EZYV15186', 'EZYV19906', 'EZYV17497', 'EZYV23213', 'EZYV18386', 'EZYV23949', 'EZYV2707', 'EZYV17838', 'EZYV24171', 'EZYV23854', 'EZYV19693', 'EZYV6676', 'EZYV10353', 'EZYV25107', 'EZYV24609', 'EZYV22496', 'EZYV22783', 'EZYV12342', 'EZYV24158', 'EZYV9489', 'EZYV10856', 'EZYV11521', 'EZYV11435', 'EZYV14195', 'EZYV20881', 'EZYV21466', 'EZYV14570', 'EZYV12358', 'EZYV17610', 'EZYV10915', 'EZYV19584', 'EZYV9296', 'EZYV23316', 'EZYV4611', 'EZYV12855', 'EZYV17783', 'EZYV302', 'EZYV14176', 'EZYV1515', 'EZYV13837', 'EZYV21015', 'EZYV5916', 'EZYV15482', 'EZYV24546', 'EZYV4395', 'EZYV20173', 'EZYV15461', 'EZYV20023', 'EZYV5825', 'EZYV453', 'EZYV19783', 'EZYV23206', 'EZYV11809', 'EZYV20757', 'EZYV13406', 'EZYV24692', 'EZYV18819', 'EZYV19593', 'EZYV21729', 'EZYV10403', 'EZYV12266', 'EZYV5420', 'EZYV1899', 'EZYV11210', 'EZYV13261', 'EZYV12250', 'EZYV24274', 'EZYV16858', 'EZYV16654', 'EZYV14705', 'EZYV3482', 'EZYV16659', 'EZYV24227', 'EZYV3645', 'EZYV20286', 'EZYV8244', 'EZYV338', 'EZYV3654', 'EZYV13278', 'EZYV5374', 'EZYV17277', 'EZYV5449', 'EZYV13970', 'EZYV18150', 'EZYV7630', 'EZYV8772', 'EZYV637', 'EZYV4981', 'EZYV4002', 'EZYV6758', 'EZYV8738', 'EZYV1692', 'EZYV14959', 'EZYV50', 'EZYV23227', 'EZYV20500', 'EZYV24628', 'EZYV14745', 'EZYV10690', 'EZYV11434', 'EZYV24650', 'EZYV24986', 'EZYV305', 'EZYV16122', 'EZYV126', 'EZYV18111', 'EZYV17913', 'EZYV3791', 'EZYV5701', 'EZYV2411', 'EZYV22118', 'EZYV23127', 'EZYV17989', 'EZYV24128', 'EZYV8335', 'EZYV12334', 'EZYV20105', 'EZYV13700', 'EZYV13206', 'EZYV22910', 'EZYV1766', 'EZYV19102', 'EZYV21772', 'EZYV10521', 'EZYV17173', 'EZYV22215', 'EZYV9640', 'EZYV9514', 'EZYV2932', 'EZYV8582', 'EZYV21963', 'EZYV25237', 'EZYV16351', 'EZYV20149', 'EZYV25323', 'EZYV24237', 'EZYV16994', 'EZYV21376', 'EZYV1934', 'EZYV11308', 'EZYV16234', 'EZYV2436', 'EZYV12955', 'EZYV22817', 'EZYV13687', 'EZYV4691', 'EZYV14999', 'EZYV11145', 'EZYV10182', 'EZYV19670', 'EZYV23790', 'EZYV2913', 'EZYV17737', 'EZYV12489', 'EZYV16275', 'EZYV14266', 'EZYV7746', 'EZYV12903', 'EZYV3090', 'EZYV12163', 'EZYV10470', 'EZYV10953', 'EZYV18597', 'EZYV1376', 'EZYV15656', 'EZYV21243', 'EZYV11388', 'EZYV9251', 'EZYV8006', 'EZYV19524', 'EZYV6264', 'EZYV20704', 'EZYV22024', 'EZYV16847', 'EZYV10052', 'EZYV19231', 'EZYV19014', 'EZYV8918', 'EZYV13421', 'EZYV8968', 'EZYV18945', 'EZYV7781', 'EZYV24255', 'EZYV2579', 'EZYV7057', 'EZYV21004', 'EZYV8723', 'EZYV22768', 'EZYV6121', 'EZYV20766', 'EZYV20651', 'EZYV9165', 'EZYV2027', 'EZYV13745', 'EZYV8120', 'EZYV12935', 'EZYV20735', 'EZYV14068', 'EZYV5328', 'EZYV4563', 'EZYV23908', 'EZYV9229', 'EZYV17674', 'EZYV21567', 'EZYV12693', 'EZYV14033', 'EZYV20569', 'EZYV21654', 'EZYV3628', 'EZYV14640', 'EZYV18624', 'EZYV2370', 'EZYV1032', 'EZYV7393', 'EZYV12203', 'EZYV8367', 'EZYV3561', 'EZYV18689', 'EZYV19708', 'EZYV11098', 'EZYV19383', 'EZYV13', 'EZYV5606', 'EZYV20946', 'EZYV6091', 'EZYV14243', 'EZYV15812', 'EZYV4070', 'EZYV14160', 'EZYV12226', 'EZYV12188', 'EZYV1169', 'EZYV3958', 'EZYV19748', 'EZYV15297', 'EZYV15367', 'EZYV16322', 'EZYV13389', 'EZYV21992', 'EZYV20013', 'EZYV19467', 'EZYV18583', 'EZYV12534', 'EZYV18268', 'EZYV13222', 'EZYV17321', 'EZYV814', 'EZYV16420', 'EZYV12511', 'EZYV15256', 'EZYV23735', 'EZYV6790', 'EZYV406', 'EZYV11900', 'EZYV17613', 'EZYV19441', 'EZYV5618', 'EZYV9612', 'EZYV5770', 'EZYV15618', 'EZYV18687', 'EZYV9648', 'EZYV22391', 'EZYV5802', 'EZYV9025', 'EZYV23736', 'EZYV6220', 'EZYV1604', 'EZYV4341', 'EZYV11254', 'EZYV18048', 'EZYV168', 'EZYV12617', 'EZYV24121', 'EZYV7537', 'EZYV9494', 'EZYV7072', 'EZYV15715', 'EZYV1964', 'EZYV18046', 'EZYV9070', 'EZYV14671', 'EZYV11097', 'EZYV22618', 'EZYV6745', 'EZYV9606', 'EZYV8552', 'EZYV20797', 'EZYV19985', 'EZYV146', 'EZYV16903', 'EZYV7518', 'EZYV14536', 'EZYV3816', 'EZYV11311', 'EZYV20245', 'EZYV16512', 'EZYV2072', 'EZYV18255', 'EZYV21786', 'EZYV5040', 'EZYV8104', 'EZYV11719', 'EZYV18437', 'EZYV3028', 'EZYV16425', 'EZYV13338', 'EZYV24350', 'EZYV17280', 'EZYV5610', 'EZYV2251', 'EZYV3301', 'EZYV6789', 'EZYV22914', 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'EZYV339', 'EZYV18317', 'EZYV15603', 'EZYV22156', 'EZYV24477', 'EZYV325', 'EZYV10796', 'EZYV206', 'EZYV6356', 'EZYV23971', 'EZYV25436', 'EZYV1842', 'EZYV1736', 'EZYV4950', 'EZYV22085', 'EZYV12678', 'EZYV10326', 'EZYV16280', 'EZYV20815', 'EZYV14800', 'EZYV5322', 'EZYV6110', 'EZYV11615', 'EZYV24709', 'EZYV5277', 'EZYV14904', 'EZYV8198', 'EZYV21012', 'EZYV13202', 'EZYV2776', 'EZYV11590', 'EZYV7291', 'EZYV9853', 'EZYV11675', 'EZYV9144', 'EZYV7978', 'EZYV23280', 'EZYV13151', 'EZYV20265', 'EZYV7976', 'EZYV12914', 'EZYV3673', 'EZYV20900', 'EZYV21626', 'EZYV15289', 'EZYV11010', 'EZYV5682', 'EZYV18148', 'EZYV25264', 'EZYV723', 'EZYV25211', 'EZYV9430', 'EZYV3799', 'EZYV13172', 'EZYV18403', 'EZYV22265', 'EZYV3985', 'EZYV5698', 'EZYV23237', 'EZYV23519', 'EZYV19093', 'EZYV14322', 'EZYV9595', 'EZYV16436', 'EZYV19622', 'EZYV9765', 'EZYV18250', 'EZYV11927', 'EZYV1915', 'EZYV22013', 'EZYV4444', 'EZYV14305', 'EZYV4140', 'EZYV13635', 'EZYV14290', 'EZYV17404', 'EZYV1183', 'EZYV10635', 'EZYV11732', 'EZYV14183', 'EZYV21252', 'EZYV16782', 'EZYV2995', 'EZYV22207', 'EZYV20731', 'EZYV15183', 'EZYV25110', 'EZYV1475', 'EZYV13946', 'EZYV18002', 'EZYV18708', 'EZYV13886', 'EZYV23065', 'EZYV907', 'EZYV411', 'EZYV4378', 'EZYV11018', 'EZYV25247', 'EZYV16779', 'EZYV14228', 'EZYV24919', 'EZYV24560', 'EZYV6278', 'EZYV17180', 'EZYV25065', 'EZYV10222', 'EZYV20116', 'EZYV10512', 'EZYV23283', 'EZYV7490', 'EZYV13010', 'EZYV22159', 'EZYV6457', 'EZYV20674', 'EZYV23926', 'EZYV12420', 'EZYV24763', 'EZYV5986', 'EZYV6007', 'EZYV15241', 'EZYV23860', 'EZYV21291', 'EZYV5349', 'EZYV18578', 'EZYV21412', 'EZYV2156', 'EZYV4623', 'EZYV24094', 'EZYV1864', 'EZYV12124', 'EZYV22733', 'EZYV13579', 'EZYV3613', 'EZYV16080', 'EZYV8699', 'EZYV16700', 'EZYV24982', 'EZYV13442', 'EZYV15163', 'EZYV17181', 'EZYV18094', 'EZYV13213', 'EZYV14786', 'EZYV15716', 'EZYV19487', 'EZYV21976', 'EZYV11663', 'EZYV12532', 'EZYV21070', 'EZYV14139', 'EZYV14983', 'EZYV21523', 'EZYV15504', 'EZYV11665', 'EZYV9818', 'EZYV7324', 'EZYV24459', 'EZYV11397', 'EZYV6387', 'EZYV335', 'EZYV24512', 'EZYV3160', 'EZYV4177', 'EZYV23400', 'EZYV13160', 'EZYV15235', 'EZYV16362', 'EZYV21852', 'EZYV15303', 'EZYV3221', 'EZYV16961', 'EZYV22544', 'EZYV3424', 'EZYV12351', 'EZYV21759', 'EZYV12520', 'EZYV6141', 'EZYV8083', 'EZYV19877', 'EZYV9456', 'EZYV12794', 'EZYV20167', 'EZYV22545', 'EZYV25170', 'EZYV19756', 'EZYV5252', 'EZYV9109', 'EZYV15110', 'EZYV17494', 'EZYV23417', 'EZYV13937', 'EZYV24565', 'EZYV2571', 'EZYV6969', 'EZYV11366', 'EZYV5928', 'EZYV18206', 'EZYV12973', 'EZYV14532', 'EZYV4608', 'EZYV24506', 'EZYV15334', 'EZYV17423', 'EZYV18737', 'EZYV10085', 'EZYV7810', 'EZYV103', 'EZYV3864', 'EZYV5979', 'EZYV12636', 'EZYV13783', 'EZYV19663', 'EZYV3730', 'EZYV6767', 'EZYV14586', 'EZYV24828'}
Unique values for continent: {'Europe', 'Asia', 'Africa', 'Oceania', 'North America', 'South America'}
Unique values for education_of_employee: {'Doctorate', 'High School', "Master's", "Bachelor's"}
Unique values for has_job_experience: {'Y', 'N'}
Unique values for requires_job_training: {'Y', 'N'}
Unique values for no_of_employees: {12, 13, 15, 16, 18, 19, 20, 21, 22, 23, 24, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 98336, 40, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 131192, 128, 129, 130, 131, 32899, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 98447, 146, 147, 148, 149, 150, 151, 145, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 33130, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 33187, 424, 425, 426, 427, 423, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 33345, 578, 577, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 131683, 612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 127, 642, 643, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 66191, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 131738, 673, 674, 675, 676, 677, 678, 672, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691, 692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 66319, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 33570, 807, 808, 809, 810, 811, 812, 806, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 132142, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1071, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098, 1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120, 1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131, 1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153, 1154, 1155, 1156, 1157, 1158, 1159, 1160, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1189, 1190, 1191, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 99503, 1200, 1199, 1202, 1203, 1204, 1205, 1201, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1217, 1218, 1219, 1220, 1221, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 1256, 1257, 1258, 1259, 1260, 1261, 1262, 1263, 1264, 1265, 1266, 1267, 1268, 1269, 1270, 1271, 1272, 1273, 1274, 1275, 1276, 1277, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1292, 1293, 1294, 1295, 1296, 1297, 1298, 1299, 1300, 34069, 1301, 1303, 1304, 1305, 1306, 1307, 1308, 1309, 1310, 1311, 1312, 1313, 1314, 1315, 1316, 1317, 1318, 1319, 1320, 1321, 1322, 1323, 1324, 1325, 1326, 1327, 1328, 1329, 1330, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1344, 1345, 1346, 1347, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1384, 1385, 1386, 1387, 1388, 1389, 1390, 1391, 1392, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1400, 1401, 1402, 1403, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 66948, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 1442, 1443, 1444, 1445, 1446, 1447, 1448, 1449, 1450, 1451, 1452, 1453, 1454, 1455, 1456, 1457, 1458, 1459, 1460, 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 1470, 1471, 1472, 34236, 1474, 1475, 1476, 1477, 1478, 1479, 1480, 1481, 1473, 1483, 1484, 1485, 1486, 1487, 1488, 1489, 1490, 1491, 1492, 1493, 1494, 1495, 1496, 1497, 1498, 1499, 1500, 1501, 1502, 1503, 1504, 1505, 1506, 1507, 1508, 1509, 1510, 1511, 1512, 1513, 1514, 1515, 1516, 1517, 1518, 1519, 1520, 1521, 1522, 1523, 1524, 1525, 1526, 1527, 1528, 1529, 1530, 1531, 1532, 1533, 1534, 1535, 1536, 1537, 1538, 1539, 1540, 1541, 1542, 1543, 1544, 1545, 1546, 1547, 1548, 1549, 1550, 1551, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 99863, 1566, 1567, 1568, 1569, 1570, 1571, 1572, 1573, 1574, 1575, 1576, 1577, 1578, 34346, 1580, 1581, 1582, 1583, 1584, 1585, 1586, 1587, 1588, 1589, 1590, 1591, 1592, 1593, 1594, 1595, 1596, 1597, 1598, 1599, 1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1608, 1609, 1610, 1611, 1612, 1613, 1614, 1615, 1616, 1617, 1618, 1619, 1620, 1621, 1622, 1623, 1624, 1625, 1626, 1627, 1628, 1629, 1630, 1631, 1632, 1633, 1634, 1635, 1636, 1637, 1638, 1639, 1640, 1641, 1642, 1643, 1644, 1645, 1646, 1647, 1648, 1649, 1650, 1651, 1652, 1653, 1654, 1655, 1656, 1657, 1658, 1659, 1660, 1661, 1662, 1663, 1664, 1665, 1666, 1667, 1668, 1669, 1670, 1671, 1672, 1673, 1674, 1675, 1676, 1677, 1678, 1679, 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22543, 22546, 22548, 22558, 22576, 251967, 22618, 120925, 22645, 22649, 88234, 55504, 252121, 22778, 88326, 22799, 22840, 252242, 22880, 547172, 22888, 22895, 22901, 55693, 88484, 22952, 22953, 55730, 55812, 23048, 4586, 88622, 23087, 23088, 154164, 23123, 252500, 55914, 23151, 23191, 23197, 121515, 88751, 23239, 121550, 23246, 23287, 154373, 23324, 23335, 4646, 23381, 88939, 121722, 23419, 56246, 89016, 23483, 23488, 56287, 23534, 23573, 23581, 56351, 23612, 56385, 23617, 89179, 23645, 23653, 121968, 23694, 23703, 23746, 122064, 122114, 23824, 23833, 89379, 23858, 23874, 23885, 23890, 89435, 23932, 23942, 122255, 23970, 23975, 56774, 24069, 24084, 24092, 24124, 24139, 24190, 102516, 24299, 57070, 24304, 24321, 24335, 89925, 253772, 24399, 57179, 581468, 24413, 122760, 57272, 24518, 57288, 24534, 24536, 90161, 90178, 90181, 24655, 57424, 221303, 135173, 90282, 90320, 24801, 188650, 24850, 24902, 70082, 155999, 24942, 24951, 24966, 90532, 90533, 25078, 25091, 25094, 25108, 25111, 25121, 90672, 57909, 25149, 25158, 57944, 123482, 25193, 25200, 25219, 25237, 90791, 25255, 58059, 25430, 25436, 25536, 25567, 123876, 156645, 91148, 25618, 91203, 25733, 91288, 25782, 58550, 25805, 386278, 91386, 58630, 91435, 25904, 25914, 91451, 124220, 25920, 25923, 25929, 25936, 25971, 25983, 25994, 5168, 5173, 26031, 26052, 26125, 26126, 124464, 124465, 58950, 58990, 124534, 26238, 26241, 5218, 26297, 26298, 59095, 190167, 91879, 517884, 288512, 91938, 59255, 26494, 26503, 26537, 26539, 26549, 26592, 26600, 59391, 157712, 26646, 26685, 26692, 223314, 26716, 26737, 92290, 26784, 59581, 26822, 59611, 92407, 26891, 26901, 26903, 26906, 125220, 92482, 26957, 59742, 26982, 92520, 103058, 27001, 59776, 256392, 59816, 27068, 27072, 27073, 59872, 125415, 453142, 27195, 125501, 92770, 27239, 92780, 256640, 27264, 92872, 60186, 93018, 27484, 60303, 93173, 27673, 27749, 27752, 60523, 60548, 27783, 421062, 27863, 27873, 27882, 27957, 27959, 27981, 93525, 28006, 28007, 28027, 28035, 28040, 355726, 28059, 28060, 28082, 28098, 60905, 126451, 60926, 28181, 28199, 93759, 28257, 28279, 126587, 28315, 28350, 61171, 28418, 61191, 61240, 94019, 28587, 28592, 61396, 192494, 28704, 28714, 28730, 61504, 61546, 28794, 94336, 28826, 28850, 28870, 28871, 94411, 28891, 28900, 28916, 127234, 28963, 127274, 28979, 28992, 29037, 94579, 29062, 94603, 29082, 29086, 94644, 389595, 29163, 29186, 29201, 29233, 29248, 127571, 127586, 29369, 29396, 29434, 29448, 29453, 62229, 29475, 29528, 62306, 29555, 29560, 62359, 62401, 62408, 29672, 95250, 29728, 29736, 29765, 95308, 95347, 128164, 62632, 29868, 128236, 29938, 29941, 29948, 30003, 128356, 95621, 128431, 30144, 30146, 30172, 62949, 95737, 30305, 95849, 30328, 30339, 95881, 128655, 128666, 30363, 30392, 95941, 30441, 194312, 96022, 96042, 128884, 30615, 30637, 30650, 63422, 63430, 30662, 358352, 30673, 30690, 30691, 30710, 30715, 129020, 30722, 30724, 30748, 30859, 30881, 30908, 30916, 30923, 63731, 31005, 31075, 96629, 31115, 162245, 96728, 129601, 96907, 31378, 64186, 326338, 31435, 31455, 31461, 97022, 162569, 31502, 31523, 97122, 129897, 31672, 97234, 31730, 195656, 31830, 31841, 97391, 31859, 64632, 64658, 130259, 64804, 32057, 32162, 130489, 32187, 196058, 32250, 32274, 65092, 32415, 65207, 32503, 98046, 32524, 65340, 32583, 32585, 32619, 32625, 98170, 98210, 32723, -26, -25, -17, -14, -11, 131070}
Unique values for yr_of_estab: {1800, 1801, 1804, 1807, 1809, 1810, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824, 1830, 1831, 1832, 1833, 1834, 1836, 1837, 1838, 1839, 1840, 1841, 1842, 1843, 1845, 1846, 1847, 1848, 1849, 1850, 1851, 1852, 1853, 1854, 1855, 1856, 1857, 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1891, 1892, 1893, 1894, 1895, 1896, 1897, 1898, 1899, 1900, 1901, 1902, 1903, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1912, 1913, 1914, 1915, 1916, 1917, 1918, 1919, 1920, 1921, 1922, 1923, 1924, 1925, 1926, 1927, 1928, 1929, 1930, 1931, 1932, 1933, 1934, 1935, 1936, 1937, 1938, 1939, 1940, 1941, 1942, 1943, 1944, 1945, 1946, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016}
Unique values for region_of_employment: {'South', 'West', 'Midwest', 'Northeast', 'Island'}
Unique values for prevailing_wage: {131073.31, 5.0899, 21.1327, 131094.31, 31.1755, 131105.0, 131106.68, 40.1191, 43.7525, 262189.0, 48.1309, 50.2928, 52.8519, 131126.46, 54.9196, 56.6515, 58.7389, 63.7364, 131136.69, 131140.5, 69.6449, 73.582, 74.2851, 74.4049, 77.3424, 78.6823, 89.5992, 131162.72, 92.6112, 97.3218, 99.3492, 100.0, 101.56, 100.93, 103.93, 104.56, 105.81, 106.25, 105.87, 108.31, 109.32, 109.06, 109.0, 110.5, 108.81, 108.12, 115.81, 116.75, 116.0, 116.56, 114.12, 120.56, 115.12, 122.57, 116.37, 124.1585, 118.43, 120.93, 127.31, 128.01, 128.37, 130.69, 130.0398, 127.3734, 131.6884, 128.12, 135.38, 136.56, 129.62, 138.69, 138.76, 140.82, 141.3424, 141.51, 139.8032, 144.76, 145.1134, 145.74, 138.875, 143.346, 149.4644, 147.0, 142.8752, 152.89, 147.1202, 154.26, 155.0951, 155.738, 131229.32, 158.3807, 158.5, 158.36, 156.8268, 159.2978, 163.07, 160.3648, 165.2722, 165.8365, 131234.61, 168.0793, 168.62, 131241.28, 262314.42, 168.1558, 173.5091, 174.594, 173.3297, 175.36, 177.89, 178.0827, 177.8352, 174.0, 181.3927, 179.62, 181.87, 178.8675, 176.76, 184.74, 131259.96, 187.0879, 189.0437, 188.6818, 182.8141, 192.37, 193.0432, 194.01, 194.503, 196.4013, 197.25, 197.9846, 199.5532, 192.06, 202.4744, 203.4146, 131275.93, 205.6246, 206.3, 207.1837, 208.5263, 209.88, 210.5073, 131278.27, 205.4479, 205.3793, 214.93, 215.1947, 216.4821, 213.4232, 218.38, 218.3542, 220.5704, 221.784, 221.1209, 222.8496, 222.6868, 225.6413, 226.0, 227.1764, 227.7353, 225.6105, 230.327, 230.4357, 231.938, 233.9425, 232.808, 131304.29, 234.4957, 131309.08, 131310.96, 230.14, 236.861, 239.6427, 242.0347, 243.2901, 131315.37, 131316.01, 246.81, 247.0206, 248.01, 248.6583, 250.8438, 131322.09, 252.43, 253.253, 254.937, 246.3592, 248.3727, 257.6062, 255.9271, 259.52, 259.4118, 261.9979, 261.5594, 260.6671, 261.92, 265.7185, 259.2239, 262.4586, 268.1379, 269.9173, 268.7924, 270.2818, 269.8894, 273.2008, 267.1333, 131347.64, 276.7576, 277.9945, 278.5388, 279.0018, 280.2427, 275.0627, 281.1569, 283.8, 283.4567, 285.1525, 284.0018, 287.2183, 288.4735, 289.8, 287.8058, 291.2804, 289.1887, 293.3152, 293.0452, 294.882, 292.0427, 297.1948, 298.1357, 299.6081, 298.8956, 300.9051, 301.2781, 303.0795, 301.0897, 131377.15, 300.0063, 307.1653, 307.9049, 309.606, 310.6986, 311.1659, 311.3324, 313.835, 314.6393, 314.2371, 316.8972, 317.7013, 318.6505, 319.36, 314.4384, 312.7783, 322.6621, 323.7, 324.2135, 325.1087, 325.1749, 327.9435, 328.9512, 329.1874, 330.3743, 329.0075, 328.9246, 325.346, 334.46, 131407.3, 336.9514, 335.5486, 338.5595, 338.2191, 339.0696, 341.0229, 341.34, 343.3205, 344.2022, 338.0171, 342.4484, 347.131, 341.808, 343.0688, 350.0159, 350.9681, 352.65, 352.4315, 131422.21, 355.613, 356.8087, 357.8194, 358.8772, 359.9641, 360.0767, 359.9667, 359.1049, 358.263, 357.8213, 361.8696, 359.5816, 367.31, 367.9114, 368.85, 370.3349, 371.8531, 363.7005, 373.2045, 373.7218, 375.6911, 373.2143, 375.6034, 377.4307, 379.87, 380.995, 381.7361, 382.2433, 383.3918, 381.6072, 385.796, 386.4868, 386.1515, 386.2526, 386.95, 390.1588, 391.8694, 390.8642, 393.4729, 394.2187, 395.995, 394.9713, 396.9152, 398.5782, 399.4489, 399.6297, 401.5855, 393.58, 403.7631, 404.4565, 403.334, 406.05, 131476.26, 131479.38, 408.9849, 410.1235, 411.2051, 412.3476, 410.6198, 414.5323, 414.9863, 408.9777, 409.975, 418.2298, 419.1893, 418.8264, 421.4584, 419.7764, 423.9009, 424.509, 424.08, 425.0178, 419.1758, 428.7372, 429.95, 429.7443, 431.8261, 431.6055, 433.2396, 434.3017, 434.3963, 436.5722, 430.8194, 438.855, 131510.83, 439.0866, 441.5777, 439.9897, 439.8307, 435.5681, 445.7888, 446.24, 445.4899, 448.9782, 449.4514, 450.9764, 451.5117, 452.2859, 452.037, 451.6964, 455.2163, 452.1367, 449.2679, 458.6381, 459.9704, 459.4009, 459.7524, 459.6712, 463.8686, 463.3112, 458.9084, 466.3655, 467.5247, 468.4512, 469.7371, 467.24, 470.6299, 464.6041, 473.5168, 474.9025, 475.2464, 476.24, 476.7579, 478.367, 471.7934, 474.047, 481.5371, 482.7664, 483.9891, 483.0851, 484.6503, 131557.74, 480.3174, 488.9859, 489.9182, 490.1568, 491.9991, 492.6745, 491.9346, 491.9008, 494.9521, 496.4588, 488.1947, 490.3426, 499.2207, 494.6279, 496.2104, 502.9275, 503.1522, 504.3171, 504.8187, 504.72, 507.93, 508.8883, 509.5368, 510.5607, 511.4655, 131584.5, 513.7152, 505.8025, 515.4184, 513.4421, 517.271, 518.0265, 511.5375, 519.1258, 521.0385, 521.7731, 523.6527, 517.5769, 525.9312, 518.531, 527.8901, 527.2303, 529.1105, 530.5959, 531.7683, 529.3875, 530.4704, 528.185, 527.6657, 536.0869, 537.2994, 529.5431, 539.4964, 539.8384, 536.553, 540.33, 543.7175, 544.0921, 543.5521, 546.2118, 547.5103, 544.2801, 543.7154, 546.5987, 551.779, 552.6891, 553.6915, 554.7338, 553.2211, 547.0719, 131629.78, 558.5524, 559.6169, 560.2798, 560.1519, 560.7972, 559.6412, 564.0663, 564.7043, 566.4743, 567.7643, 566.1703, 560.5899, 570.926, 571.9348, 572.8227, 572.514, 574.2465, 574.1964, 576.97, 575.3468, 574.55, 579.2598, 577.8283, 581.9219, 581.855, 583.7382, 579.3819, 583.3376, 582.45, 587.7049, 588.3444, 589.4545, 590.0203, 591.7602, 592.2029, 590.56, 594.7953, 588.3469, 596.2488, 597.9, 596.2339, 596.5164, 600.4991, 601.0742, 602.5, 597.273, 603.0547, 605.1812, 604.0816, 599.6041, 603.4404, 604.1603, 610.5874, 611.2748, 610.655, 613.71, 614.7183, 615.0013, 616.5072, 617.3676, 615.3414, 615.4815, 618.2058, 621.3853, 622.9059, 617.3345, 624.0905, 625.844, 626.7561, 627.9543, 624.8694, 629.7126, 630.66, 630.2337, 631.7448, 631.6161, 634.1042, 633.6646, 635.9458, 637.4872, 638.02, 631.3547, 640.6397, 640.8977, 642.6508, 643.4375, 641.4029, 645.353, 646.7635, 644.884, 645.2913, 649.6394, 649.5659, 649.1376, 652.7163, 653.4797, 651.2278, 655.9658, 656.6898, 649.44, 658.0041, 656.3975, 660.6679, 661.8469, 660.5249, 656.0788, 664.6442, 665.1571, 661.0799, 667.4133, 668.4393, 662.7582, 670.6567, 671.4491, 672.6842, 673.8178, 672.5571, 131745.49, 676.2335, 677.0829, 678.6486, 674.0447, 680.9274, 680.497, 682.8026, 683.6176, 675.3657, 131757.53, 686.4229, 679.3129, 688.1266, 688.7327, 690.3663, 691.1057, 692.4637, 692.9511, 131764.85, 689.9989, 693.8824, 697.8354, 698.9778, 698.2524, 698.944, 698.7048, 702.8717, 702.9446, 704.06, 705.3218, 706.1603, 707.433, 262852.71, 708.9617, 701.5948, 708.7259, 712.2584, 713.7807, 714.0764, 713.0041, 716.5913, 717.5354, 718.027, 718.6797, 131792.99, 721.5274, 719.9582, 723.5006, 724.0276, 723.9761, 724.3166, 724.9628, 727.7027, 729.8391, 730.4062, 727.0036, 730.6864, 733.98, 734.1893, 735.9425, 733.4222, 737.4472, 738.1177, 739.7268, 737.7488, 737.7518, 741.0381, 743.4468, 743.7198, 742.6434, 746.5986, 746.2808, 748.0707, 741.6666, 743.7925, 745.163, 747.7824, 753.3927, 131826.56, 755.2588, 756.9234, 750.5135, 758.7597, 759.4199, 758.771, 761.4506, 762.0136, 763.908, 764.8895, 131836.07, 766.8536, 767.9829, 768.4136, 769.4869, 131842.91, 771.2467, 770.0452, 773.3891, 773.1065, 774.2597, 776.9489, 777.8201, 774.708, 771.6902, 780.696, 781.7726, 782.5254, 783.4324, 784.2604, 784.9041, 786.5458, 786.188, 785.764, 783.8797, 786.5978, 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122187.35, 122195.12, 122203.35, 122215.53, 122222.53, 122231.2, 122239.88, 122240.98, 122240.81, 122255.17, 122257.16, 122257.96, 122260.05, 122262.21, 122283.58, 122284.82, 122293.19, 122310.01, 122323.65, 122324.22, 122330.0, 122332.69, 122349.51, 122353.74, 122358.8, 122360.7, 122376.73, 122380.57, 122387.28, 122388.47, 122395.57, 122414.77, 122418.29, 122424.6, 122440.88, 122454.82, 122465.21, 122474.72, 122479.42, 122482.33, 122506.94, 122511.0, 122528.95, 122535.0, 122540.55, 122561.09, 122565.13, 122566.14, 122580.62, 122582.6, 122588.72, 122590.93, 122591.85, 253668.86, 122615.23, 122616.55, 122622.08, 122624.42, 253698.68, 122629.45, 122630.54, 122642.28, 122648.11, 122651.65, 122663.94, 122674.76, 122684.72, 122685.88, 122691.96, 122691.02, 122695.72, 122696.23, 122697.43, 122703.66, 122711.51, 122716.68, 122720.12, 122745.13, 122759.79, 122769.34, 122770.0, 122771.52, 122774.89, 122784.9, 122812.56, 122814.02, 122819.19, 122828.94, 122829.47, 253915.96, 122848.3, 122855.65, 122859.37, 122865.02, 122890.56, 122896.09, 122898.11, 122899.51, 122902.21, 122903.03, 122912.17, 122927.88, 122935.37, 122936.33, 254015.02, 122952.87, 122959.98, 122959.28, 122963.12, 122965.96, 122993.83, 122996.86, 123017.61, 123018.45, 123023.1, 123039.99, 123040.54, 123045.14, 123048.91, 123049.88, 123049.55, 254157.54, 123087.21, 123092.29, 123092.38, 123101.33, 123102.04, 123118.78, 123120.05, 123125.64, 123130.98, 123144.8, 123148.09, 123157.63, 254230.21, 123175.08, 123176.33, 123187.32, 123194.1, 254275.73, 123214.48, 123222.87, 123238.17, 123244.52, 123258.12, 123259.44, 123266.89, 123273.68, 123276.25, 123285.69, 254361.39, 123297.27, 123302.86, 123316.75, 123329.52, 123330.2, 123333.42, 123346.27, 123360.97, 123374.86, 123380.14, 123381.6, 123407.28, 123417.87, 107.1327, 123423.01, 123437.12, 123437.83, 123438.97, 123440.24, 123447.15, 123447.58, 254522.41, 123457.29, 123468.56, 123475.42, 123480.64, 123480.07, 123482.25, 123490.53, 123502.09, 123505.66, 123519.71, 123523.22, 254604.08, 123539.66, 123541.71, 123544.17, 123553.91, 123555.45, 123565.74, 123565.21, 123584.93, 123597.59, 123620.16, 123631.14, 123637.31, 123640.01, 123642.69, 123654.65, 123656.77, 123673.55, 123684.49, 123688.79, 123689.54, 123688.44, 123692.21, 123699.45, 123706.67, 123707.36, 123711.01, 123721.58, 170.9434, 123737.54, 123739.41, 123744.25, 123748.04, 123752.0, 123756.6, 123773.37, 123775.68, 123783.66, 254858.86, 254861.76, 123826.18, 123836.68, 123845.84, 123856.7, 123870.96, 123879.29, 123882.1, 123908.67, 123918.91, 123919.34, 123928.02, 123931.2, 123939.49, 123940.77, 123952.35, 123953.16, 123956.57, 123957.0, 123961.78, 255033.75, 123963.22, 123966.37, 123970.66, 123974.14, 123984.95, 123993.41, 124015.94, 124028.03, 124028.96, 124031.52, 124051.23, 124056.32, 124082.68, 124085.73, 124092.0, 124102.09, 124108.52, 124127.0, 124129.32, 124133.29, 124138.9, 124138.64, 124148.0, 124155.88, 124155.24, 254.8773, 124160.54, 124168.49, 124187.69, 124201.98, 124211.98, 124215.92, 124218.44, 124220.84, 124235.53, 124244.19, 124252.65, 124264.83, 124265.9, 124266.45, 124267.16, 124270.78, 124286.96, 124298.43, 124312.58, 124314.21, 124316.92, 124317.33, 124318.91, 124370.98, 124370.09, 124382.47, 124401.37, 124428.23, 124434.41, 124435.47, 124439.76, 124449.18, 124457.5, 124460.67, 124480.48, 318.5846, 124489.06, 124495.5, 124511.86, 124524.31, 124525.38, 124525.47, 124525.43, 124529.59, 124531.35, 124541.48, 124550.61, 255626.72, 124573.61, 124581.3, 124586.62, 124587.31, 124591.19, 341.7415, 255674.94, 124604.5, 124628.13, 124658.25, 124659.16, 124659.28, 124663.67, 124668.62, 124682.1, 124682.81, 124686.12, 359.7006, 255771.26, 124705.74, 124709.19, 364.9326, 124715.65, 124716.6, 124729.03, 124745.51, 124746.01, 371.8256, 124760.92, 124778.03, 124798.99, 124801.84, 124810.53, 124823.44, 124829.82, 124830.48, 124851.97, 124864.48, 124870.32, 124873.74, 124878.65, 124883.71, 124893.09, 124895.0, 124900.16, 124910.26, 124922.15, 124925.02, 124934.6, 124937.02, 124940.96, 124943.29, 124945.3, 124947.51, 124956.98, 124957.72, 413.3845, 124960.86, 124968.16, 124973.21, 124974.58, 124988.32, 124993.47, 124995.72, 124998.3, 125014.69, 125015.28, 125029.22, 125034.41, 125038.9, 125070.57, 125070.15, 125076.08, 125086.67, 125088.46, 125089.36, 125096.16, 125097.25, 125106.92, 125113.27, 125115.8, 125122.1, 125123.24, 125127.24, 125130.14, 256205.38, 125144.74, 125172.89, 125175.16, 125178.21, 256261.78, 125189.0, 125193.02, 125201.1, 125222.77, 125230.19, 125240.69, 125246.83, 125284.22, 125290.11, 125311.45, 125320.77, 125323.7, 125325.51, 125336.9, 125345.26, 125349.52, 125352.49, 125357.27, 496.3899, 125377.72, 125382.31, 125384.93, 125393.1, 125395.07, 501.3166, 125403.68, 125403.09, 125406.43, 125412.24, 505.6952, 125422.28, 256502.28, 125454.81, 125457.0, 125465.83, 125470.03, 125472.25, 125473.59, 125481.66, 518.3827, 125489.91, 125496.73, 125498.7, 125499.14, 115.1174, 125531.29, 125539.15, 256618.11, 125560.71, 125569.1, 128.6745, 125574.83, 125579.78, 125601.58, 541.2883, 125612.2, 125624.14, 125625.87, 125624.38, 125655.65, 125661.75, 125661.19, 125683.03, 125691.0, 125702.65, 125704.68, 125729.45, 125731.81, 125732.64, 125732.19, 125740.7, 125750.1, 125757.81, 125796.26, 125796.3, 125796.82, 125799.78, 125807.4, 125817.82, 125820.7, 256903.7, 125840.4, 125849.89, 125850.71, 125865.48, 125869.86, 125880.21, 125880.68, 125908.42, 125913.28, 125916.86, 125925.71, 125926.18, 125939.31, 609.8755, 125954.21, 125960.81, 613.8004, 125966.68, 125970.79, 125978.46, 125987.84, 620.9771, 126008.86, 126016.18, 126022.16, 126031.84, 126040.08, 126044.24, 126049.56, 126055.3, 632.4488, 633.679, 126070.95, 126072.88, 126073.98, 126098.49, 126099.74, 126106.35, 644.1129, 126122.47, 126123.21, 126123.79, 645.8773, 126145.21, 126150.37, 126153.78, 126174.98, 126189.3, 126194.09, 661.4007, 126215.33, 126224.41, 126225.3, 126232.53, 126243.05, 126244.31, 126258.45, 672.0522, 126265.26, 126269.34, 126270.65, 126293.71, 126307.46, 126315.79, 126319.2, 126322.08, 685.554, 126329.96, 126336.28, 126350.08, 126351.57, 126352.88, 690.4362, 126371.14, 126375.0, 126379.02, 126379.64, 126381.75, 126399.34, 705.4771, 126431.51, 707.813, 126453.44, 126460.74, 126473.89, 126481.29, 126497.4, 126498.81, 126503.87, 126506.11, 126511.45, 126516.39, 126517.52, 126532.11, 126533.17, 126538.01, 728.9416, 126548.59, 126549.52, 122.6374, 126569.38, 126590.38, 126593.79, 126597.1, 126612.68, 126622.47, 126668.89, 126673.41, 126684.93, 126693.97, 257775.44, 257781.92, 126716.84, 126721.77, 126724.78, 126727.45, 126739.96, 126744.96, 126744.18, 126746.79, 126761.15, 126768.92, 126778.92, 126783.3, 126783.22, 126796.19, 126813.75, 126822.93, 126826.57, 787.1327, 126840.03, 257916.69, 126845.21, 789.4758, 126858.8, 126861.43, 126862.33, 126875.34, 795.947, 126884.34, 126892.33, 257966.97, 126895.3, 126906.32, 126912.71, 126916.75, 802.4758, 126921.34, 257994.73, 126928.13, 126935.9, 126935.24, 258044.72, 126992.77, 127012.42, 127063.5, 127064.3, 127065.45, 127069.45, 833.2078, 127078.0, 127079.96, 127084.5, 127085.06, 127096.87, 127110.13, 127122.93, 844.7739, 127163.7, 127169.72, 127174.77, 127180.67, 127185.34, 127192.61, 127192.55, 127198.53, 127209.06, 127214.67, 258292.84, 127222.23, 127234.69, 127242.79, 127246.72, 127247.75, 127248.19, 127254.87, 127260.07, 127260.47, 127271.51, 127273.02, 127279.96, 127279.97, 127288.25, 127296.66, 127303.96, 127312.41, 127325.21, 127333.13, 127346.84, 127352.29, 127370.6, 127380.63, 127381.46, 127396.37, 127403.07, 899.036, 258477.93, 127408.62, 127425.43, 127429.32, 127430.73, 127431.99, 127437.12, 127437.06, 127452.13, 127468.54, 127472.09, 127475.66, 127480.33, 127490.32, 127491.16, 127500.61, 127517.66, 127517.18, 127535.86, 127542.64, 127548.46, 930.6453, 127564.06, 127580.56, 127581.78, 127597.92, 127610.62, 127619.42, 127639.05, 127648.26, 127655.87, 127671.84, 127678.25, 127682.93, 127695.19, 127706.35, 127740.34, 127752.53, 127757.84, 127759.73, 127763.96, 127765.0, 127765.36, 127771.9, 127772.02, 127774.69, 127778.82, 127822.96, 127826.35, 127828.06, 171.0931, 127856.8, 127856.09, 127862.13, 127868.01, 127885.35, 127887.26, 127890.8, 127892.96, 127904.94, 127908.22, 127908.53, 127911.35, 127922.38, 127925.01, 127934.59, 127935.21, 127951.85, 127953.07, 127960.51, 127965.87, 127978.54, 127982.72, 127991.5, 127997.92, 128004.06, 128008.3, 259083.16, 128016.42, 128019.96, 259097.55, 128044.08, 128078.97, 128093.42, 128104.61, 128133.36, 128134.04, 128150.25, 128151.03, 128158.37, 128166.97, 128167.33, 128173.92, 128182.07, 128197.11, 128199.58, 259277.18, 128209.74, 128215.82, 128220.69, 128238.66, 128239.79, 128245.38, 128264.12, 257.6129, 128278.05, 128286.39, 128289.76, 128293.72, 128294.66, 128330.67, 128335.44, 128346.48, 128351.74, 128367.28, 128371.16, 128376.17, 128384.95, 259461.84, 128404.6, 286.8984, 128422.5, 128434.3, 128439.88, 294.402, 128461.25, 128469.65, 128470.83, 128474.33, 259556.53, 128504.69, 128505.27, 128514.42, 128525.19, 128531.63, 128538.47, 128544.2, 128547.56, 128559.73, 128568.92, 128574.6, 128587.22, 128598.74, 128600.87, 324.902, 128639.0, 128651.42, 259739.35, 337.8539, 338.42, 128683.76, 128687.82, 128694.12, 128710.47, 128711.52, 128720.83, 128735.13, 128740.56, 351.7109, 128748.38, 128758.39, 128759.76, 259835.69, 128766.79, 128782.21, 128792.45, 363.8341, 128821.76, 128824.08, 128829.03, 128831.85, 128833.78, 128847.07, 128850.75, 128852.61, 128854.16, 128870.48, 128877.33, 128883.23, 128883.16, 128890.94, 128894.18, 128901.25, 128902.6, 128911.54, 128913.55, 128923.23, 128925.29, 128927.59, 128927.12, 128931.59, 128939.46, 128946.06, 128950.94, 128952.12, 395.9807, 128981.51, 128983.07, 128983.47, 399.2752, 128986.5, 128985.38, 128999.16, 129013.48, 129017.73, 129020.11, 129020.92, 3.4889, 129032.04, 129050.28, 129058.98, 129065.47, 417.8557, 129087.05, 129094.64, 129095.51, 129105.3, 423.9645, 129110.86, 424.768, 129114.16, 129115.51, 129122.1, 129136.93, 129139.75, 433.152, 129156.7, 129166.04, 129169.76, 129184.97, 129189.46, 446.9555, 129227.39, 129234.29, 129235.76, 129240.68, 260321.86, 129255.91, 129261.95, 129265.77, 129267.0, 129276.92, 129283.7, 129289.13, 129289.47, 129294.14, 129300.86, 129307.2, 129319.09, 129321.81, 129333.24, 129346.54, 129355.58, 129365.58, 129390.69, 129391.67, 129422.72, 129425.05, 129434.63, 129436.25, 129450.64, 129452.48, 129455.03, 129458.05, 129462.04, 129462.53, 129520.23, 129521.85, 506.6736, 129528.3, 129533.41, 129541.91, 129547.83, 129554.24, 129559.22, 129562.3, 129585.98, 129615.66, 129622.52, 129627.45, 129628.39, 129632.27, 129640.76, 129647.46, 129666.34, 129670.44, 129676.9, 129679.06, 129684.53, 129695.24, 129697.07, 129698.52, 129701.94, 129704.95, 129705.95, 129715.62, 129727.5, 129731.91, 129744.35, 129748.81, 129753.18, 129765.37, 129776.33, 129778.95, 129798.65, 129800.15, 156.3903, 129811.82, 129812.63, 129812.29, 129819.33, 129822.93, 129831.54, 129842.88, 129845.83, 129862.7, 129873.18, 129883.44, 129895.13, 129897.06, 129901.45, 129904.83, 176.8885, 129919.65, 129924.38, 129941.81, 129951.66, 129952.96, 129954.04, 129972.96, 129972.45, 129987.35, 192.5117, 129996.95, 130002.47, 130008.73, 130009.87, 130008.01, 130013.75, 130069.92, 130070.64, 130081.84, 130081.59, 130093.45, 130096.45, 130099.37, 130116.43, 130132.64, 130136.12, 130137.7, 130160.33, 130161.11, 130171.7, 228.2617, 130175.47, 130181.59, 130192.56, 130209.34, 130266.41, 130279.37, 130289.74, 130305.09, 130318.3, 130320.79, 130343.26, 130352.78, 130358.75, 130362.86, 130370.42, 130385.72, 130396.85, 130396.39, 130404.72, 130409.82, 130409.56, 130415.0, 261496.17, 130429.18, 130429.73, 130441.34, 130442.88, 130443.76, 130443.9, 130446.31, 130455.91, 130456.77, 130464.01, 130465.35, 130477.23, 261554.61, 261556.56, 130491.58, 130493.92, 130510.28, 130513.76, 130515.51, 130536.55, 130538.57, 130542.7, 130544.55, 130545.32, 100.73, 101.73, 130559.63, 130562.97, 130563.12, 130565.83, 109.48, 130600.53, 130600.27, 130604.13, 111.73, 130610.73, 130611.92, 113.23, 130623.82, 130631.87, 130636.68, 130637.71, 130645.36, 130651.5, 130652.8, 261729.62, 130671.23, 130674.71, 130680.27, 130690.67, 130701.58, 130705.18, 130706.81, 130735.67, 130737.85, 130749.9, 130750.12, 130750.62, 130753.83, 130762.79, 130763.98, 130781.13, 130785.67, 130798.67, 130805.39, 130817.42, 130817.72, 130837.35, 130852.41, 130860.17, 130876.09, 130886.71, 130896.26, 130936.52, 130983.03, 130994.76, 130996.02, 131001.97, 131023.37, 131032.71, 131032.99}
Unique values for unit_of_wage: {'Month', 'Year', 'Week', 'Hour'}
Unique values for full_time_position: {'Y', 'N'}
Unique values for case_status: {'Denied', 'Certified'}

Checking value count of case_status

data['case_status'].value_counts()

case_status
Certified    17018
Denied        8462
Name: count, dtype: int64

• Classes in feature to be predicted is imbalanced

Check data types of features in the dataset

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 25480 entries, 0 to 25479
Data columns (total 12 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   case_id                25480 non-null  object 
 1   continent              25480 non-null  object 
 2   education_of_employee  25480 non-null  object 
 3   has_job_experience     25480 non-null  object 
 4   requires_job_training  25480 non-null  object 
 5   no_of_employees        25480 non-null  int64  
 6   yr_of_estab            25480 non-null  int64  
 7   region_of_employment   25480 non-null  object 
 8   prevailing_wage        25480 non-null  float64
 9   unit_of_wage           25480 non-null  object 
 10  full_time_position     25480 non-null  object 
 11  case_status            25480 non-null  object 
dtypes: float64(1), int64(2), object(9)
memory usage: 2.3+ MB

• All datatypes for the various features are ok, nothing is out of place.

• Though, the features with object as data type needs to be converted to category to reduce memoery usage and reduce computational time.

Fixing data types

#Assigning features with object data type to a variable.
cols = data.select_dtypes(['object'])
cols.columns

Index(['case_id', 'continent', 'education_of_employee', 'has_job_experience',
       'requires_job_training', 'region_of_employment', 'unit_of_wage',
       'full_time_position', 'case_status'],
      dtype='object')

#Changing object datatype to category
for i in cols.columns:
    data[i] = data[i].astype('category')

#Checking data types again
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 25480 entries, 0 to 25479
Data columns (total 12 columns):
 #   Column                 Non-Null Count  Dtype   
---  ------                 --------------  -----   
 0   case_id                25480 non-null  category
 1   continent              25480 non-null  category
 2   education_of_employee  25480 non-null  category
 3   has_job_experience     25480 non-null  category
 4   requires_job_training  25480 non-null  category
 5   no_of_employees        25480 non-null  int64   
 6   yr_of_estab            25480 non-null  int64   
 7   region_of_employment   25480 non-null  category
 8   prevailing_wage        25480 non-null  float64 
 9   unit_of_wage           25480 non-null  category
 10  full_time_position     25480 non-null  category
 11  case_status            25480 non-null  category
dtypes: category(9), float64(1), int64(2)
memory usage: 2.0 MB

• There are no more features with object data type.

Checking for missing values.

#Checking for missing values
missing_values = data.isnull().sum()
print(f'There are {missing_values.sum()} missing values')

There are 0 missing values

Checking for duplicates.

#Checking for duplicates
duplicates = data.duplicated().sum()
print(f'There are {duplicates} duplicates')

There are 0 duplicates

Statistical summary of data

#Checking statistical summary
data.describe(include='all').T

	count	unique	top	freq	mean	std	min	25%	50%	75%	max
case_id	25480	25480	EZYV01	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN
continent	25480	6	Asia	16861	NaN	NaN	NaN	NaN	NaN	NaN	NaN
education_of_employee	25480	4	Bachelor's	10234	NaN	NaN	NaN	NaN	NaN	NaN	NaN
has_job_experience	25480	2	Y	14802	NaN	NaN	NaN	NaN	NaN	NaN	NaN
requires_job_training	25480	2	N	22525	NaN	NaN	NaN	NaN	NaN	NaN	NaN
no_of_employees	25480.00000	NaN	NaN	NaN	5667.04321	22877.92885	-26.00000	1022.00000	2109.00000	3504.00000	602069.00000
yr_of_estab	25480.00000	NaN	NaN	NaN	1979.40993	42.36693	1800.00000	1976.00000	1997.00000	2005.00000	2016.00000
region_of_employment	25480	5	Northeast	7195	NaN	NaN	NaN	NaN	NaN	NaN	NaN
prevailing_wage	25480.00000	NaN	NaN	NaN	74455.81459	52815.94233	2.13670	34015.48000	70308.21000	107735.51250	319210.27000
unit_of_wage	25480	4	Year	22962	NaN	NaN	NaN	NaN	NaN	NaN	NaN
full_time_position	25480	2	Y	22773	NaN	NaN	NaN	NaN	NaN	NaN	NaN
case_status	25480	2	Certified	17018	NaN	NaN	NaN	NaN	NaN	NaN	NaN

• The oldest employer company was established in 1800, while the youngest was established in 2005.
• The highest prevailing wage was 319,210 dollars while the least prevailing wage is 2.1 dollars which is quite unusual though this could be an hourly wage.This will be further visualized later on.
• The employer company with the highest employees have 602,069 employees while the company with least number of employee have -26 employees which is obviously an error.The variance in this column is quite high.

#Identifying rows with negative number of employees in company
negative_values_data = data[data['no_of_employees'] < 0]
negative_values_data

	case_id	continent	education_of_employee	has_job_experience	requires_job_training	no_of_employees	yr_of_estab	region_of_employment	prevailing_wage	unit_of_wage	full_time_position	case_status
245	EZYV246	Europe	Master's	N	N	-25	1980	Northeast	39452.99000	Year	Y	Certified
378	EZYV379	Asia	Bachelor's	N	Y	-11	2011	Northeast	32506.14000	Year	Y	Denied
832	EZYV833	South America	Master's	Y	N	-17	2002	South	129701.94000	Year	Y	Certified
2918	EZYV2919	Asia	Master's	Y	N	-26	2005	Midwest	112799.46000	Year	Y	Certified
6439	EZYV6440	Asia	Bachelor's	N	N	-14	2013	South	103.97000	Hour	Y	Denied
6634	EZYV6635	Asia	Bachelor's	Y	N	-26	1923	West	5247.32000	Year	Y	Denied
7224	EZYV7225	Europe	Doctorate	N	N	-25	1998	Midwest	141435.95000	Year	Y	Certified
7281	EZYV7282	Asia	High School	N	N	-14	2000	Midwest	58488.50000	Year	Y	Denied
7318	EZYV7319	Asia	Bachelor's	Y	Y	-26	2006	South	115005.61000	Year	Y	Certified
7761	EZYV7762	Asia	Master's	N	N	-11	2009	Midwest	38457.51000	Year	Y	Certified
9872	EZYV9873	Europe	Master's	Y	N	-26	1996	South	37397.05000	Year	Y	Certified
11493	EZYV11494	Asia	High School	Y	N	-14	1999	South	27599.35000	Year	Y	Denied
13471	EZYV13472	North America	Master's	N	N	-17	2003	Northeast	257.24130	Hour	Y	Denied
14022	EZYV14023	Asia	Bachelor's	N	Y	-11	1946	Northeast	108403.56000	Year	Y	Certified
14146	EZYV14147	Asia	Bachelor's	N	Y	-26	1954	West	81982.27000	Year	Y	Certified
14726	EZYV14727	Asia	Master's	N	N	-11	2000	Midwest	167851.80000	Year	Y	Certified
15600	EZYV15601	Asia	Bachelor's	N	N	-14	2014	South	24641.61000	Year	Y	Denied
15859	EZYV15860	Asia	High School	N	N	-11	1969	South	44640.60000	Year	Y	Denied
16157	EZYV16158	Asia	Master's	Y	N	-11	1994	South	62681.25000	Year	Y	Certified
16883	EZYV16884	North America	Bachelor's	Y	N	-26	1968	Northeast	168.15580	Hour	Y	Denied
17006	EZYV17007	Asia	Doctorate	Y	N	-11	1984	West	25753.51000	Year	Y	Denied
17655	EZYV17656	North America	Bachelor's	Y	N	-17	2007	Northeast	129753.18000	Year	Y	Denied
17844	EZYV17845	Asia	Bachelor's	N	N	-14	2012	West	29325.85000	Year	Y	Denied
17983	EZYV17984	Asia	Bachelor's	N	N	-26	2004	South	84359.98000	Year	Y	Denied
20815	EZYV20816	Asia	Bachelor's	N	Y	-17	1990	West	91897.57000	Year	Y	Certified
20984	EZYV20985	Europe	Doctorate	Y	N	-14	1989	Midwest	37012.80000	Year	Y	Certified
21255	EZYV21256	North America	High School	N	N	-25	1987	South	99405.47000	Year	N	Denied
21760	EZYV21761	Asia	Bachelor's	Y	N	-25	2000	West	100463.58000	Year	Y	Certified
21944	EZYV21945	Africa	Master's	Y	N	-25	1977	Midwest	79150.51000	Year	Y	Certified
22084	EZYV22085	North America	Bachelor's	Y	N	-14	1980	West	691.06090	Hour	Y	Denied
22388	EZYV22389	Asia	Master's	Y	N	-14	1986	South	17893.11000	Year	Y	Certified
23186	EZYV23187	Asia	Master's	N	Y	-11	2007	Midwest	120195.35000	Year	Y	Certified
23476	EZYV23477	Europe	Master's	Y	N	-11	2000	West	95072.75000	Year	Y	Denied

• The negative values in number of employees in employer company will be handled when treating outliers.

Exploratory Data Analysis (EDA)

• EDA is an important part of any project involving data.
• It is important to investigate and understand the data better before building a model with it.
• A few questions have been mentioned below which will help you approach the analysis in the right manner and generate insights from the data.
• A thorough analysis of the data, in addition to the questions mentioned below, should be done.

Leading Questions:

1. Those with higher education may want to travel abroad for a well-paid job. Does education play a role in Visa certification?

Answer: Yes, educational level plays a big role in visa approvals, as seen in the visuals, the ratio of approvals increases with each employee higher level of education. To put in to perspective, over 80% of visa applications from employees with Doctorate were approved compared to the about 35% for those with only high school education.

1. How does the visa status vary across different continents?

Answer: There is no much difference in visa status accross the different continents,eventhough European employees slightly had more approvals than the employees from other continents.

1. Experienced professionals might look abroad for opportunities to improve their lifestyles and career development. Does work experience influence visa status?

Answer: Yes, experience profeesionals are more likely to get their visa approved compared to employees with no experience. From the data, about 75% of experienced employees had their visas approved compared to about 55% for the employees without experience.

1. In the United States, employees are paid at different intervals. Which pay unit is most likely to be certified for a visa?

Answer: The yearly pay unit is most likely to be approved, while the hourly pay unit is the least likely to be approved.

1. The US government has established a prevailing wage to protect local talent and foreign workers. How does the visa status change with the prevailing wage?

Answer: The prevailing wage does not happen to affect the approval status of the employees visa.

#Creating a copy of dataset for EDA
data1 = data.copy()
#dropping unique identifier column 'case_id'
data1 = data1.drop('case_id', axis=1)  # Remove 'case_id:' column (axis=1 for columns)

data1.columns

Index(['continent', 'education_of_employee', 'has_job_experience',
       'requires_job_training', 'no_of_employees', 'yr_of_estab',
       'region_of_employment', 'prevailing_wage', 'unit_of_wage',
       'full_time_position', 'case_status'],
      dtype='object')

Creating user defined function to carryout EDA.

#Defining custom variable t plot combine histogram and box plot
def histogram_boxplot(data, feature, figsize=(15, 10), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (15,10))
    kde: whether to show the density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data1, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a triangle will indicate the mean value of the column
    sns.histplot(
        data=data1, x=feature, kde=kde, ax=ax_hist2, bins=bins
    ) if bins else sns.histplot(
        data=data1, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data1[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data1[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram

# function to create labeled barplots


def labeled_barplot(data, feature, perc=True, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data1[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 2, 6))
    else:
        plt.figure(figsize=(n + 2, 6))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=data1,
        x=feature,
        palette="Paired",
        order=data[feature].value_counts().index[:n],
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot

#Defining function to plot stacked barplot
def stacked_barplot(data, predictor, target):
    """
    Print the category counts and plot a stacked bar chart

    data: dataframe
    predictor: independent variable
    target: target variable
    """
    count = data1[predictor].nunique()
    sorter = data1[target].value_counts().index[-1]
    tab1 = pd.crosstab(data1[predictor], data1[target], margins=True).sort_values(
        by=sorter, ascending=False
    )
    print(tab1)
    print("-" * 120)
    tab = pd.crosstab(data1[predictor], data1[target], normalize="index").sort_values(
        by=sorter, ascending=False
    )
    tab.plot(kind="bar", stacked=True, figsize=(count + 5, 5))
    plt.legend(
        loc="lower left", frameon=False,
    )
    plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
    plt.show()

# function to plot distributions wrt target


def distribution_plot_wrt_target(data, predictor, target):

    fig, axs = plt.subplots(2, 2, figsize=(12, 10))

    target_uniq = data1[target].unique()

    axs[0, 0].set_title("Distribution of target for target=" + str(target_uniq[0]))
    sns.histplot(
        data=data1[data1[target] == target_uniq[0]],
        x=predictor,
        kde=True,
        ax=axs[0, 0],
        color="teal",
        stat="density",
    )

    axs[0, 1].set_title("Distribution of target for target=" + str(target_uniq[1]))
    sns.histplot(
        data=data1[data1[target] == target_uniq[1]],
        x=predictor,
        kde=True,
        ax=axs[0, 1],
        color="orange",
        stat="density",
    )

    axs[1, 0].set_title("Boxplot w.r.t target")
    sns.boxplot(data=data1, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")

    axs[1, 1].set_title("Boxplot (without outliers) w.r.t target")
    sns.boxplot(
        data=data,
        x=target,
        y=predictor,
        ax=axs[1, 1],
        showfliers=False,
        palette="gist_rainbow",
    )

    plt.tight_layout()
    plt.show()

Univariate analysis

Visualizing for features with countinous variable

# Check for continuous data types
continuous_cols = data.select_dtypes(include=[np.number])  # Select numeric columns

# Loop through continuous columns
for col in continuous_cols.columns:
    plt.figure()  # Create a new figure for each plot
    histogram_boxplot(data1, col)  # Pass the column name (feature)

    # Extract the column title from the DataFrame
    col_title = data1.columns.get_loc(col)  # Get the column index and use it to retrieve the title
    plt.title(col_title)  # Set the plot title using the column title
    plt.show()

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

• All the features with countinous data appear to have skewwed data distribution.

Visalizing features with categorical variables

# Check for categorical data types
categorical_cols = data1.select_dtypes(include=['object'])  # Select category columns (categorical data)


# Loop through categorical columns
for col in categorical_cols.columns:
   plt.figure()  # Create a new figure for each plot

   labeled_barplot(data, col)  # Plot bar chart of categorical variables

   plt.show();

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

<Figure size 640x480 with 0 Axes>

Observations

• The continent with most employee visa application was Asia.
• Among all employee educational levels, those with Bachelor's degree formed the majority and Doctorate the least.
• The majority of employees had job experience, which further explains why many emplyees seeking visas did not require job training.
• Majority of employers sponsoring the visas where from the northeast, while the least number of employers came from the island.
• Majority of the employers listed the proposed unit wage for the employee as yearly.
• Majority of the job positions were on a full time basis.
• Majority of the visa applications were certified, Two-third to be precise.

BIVARIATE ANALYSIS

preparing data for bivariate visualization.

• Categorical variables will be encoded before been visualized on a correlaton heatmap.

data2 = data1.copy()

# Converting Boolean variables to binary coding
data2['case_status'] = data2['case_status'].replace({'Certified': 1, 'Denied': 0})
data2['full_time_position'] = data2['full_time_position'].replace({'Y': 1, 'N': 0})
data2['requires_job_training'] = data2['requires_job_training'].replace({'Y': 1, 'N': 0})
data2['has_job_experience'] = data2['has_job_experience'].replace({'Y': 1, 'N': 0})

# One-hot encoding non-ordinal features
def create_dummies(data2, specific_cols):
    """
    Creates dummy variables for specific columns in a pandas DataFrame.

    Args:
        data: The pandas DataFrame.
        specific_cols (list): A list of column names to convert to dummy variables.

    Returns:
        pandas.DataFrame: The DataFrame with new dummy variables.
    """

    # Create dummy variables for the specified columns
    dummies = pd.get_dummies(data2[specific_cols], drop_first=False)

    # Drop the original columns from the DataFrame
    data2 = data2.drop(columns=specific_cols)

    # Combine original data without the specified columns with dummy variables
    return pd.concat([data2, dummies], axis=1)


specific_cols = ['unit_of_wage', 'region_of_employment', 'continent']  # Columns to convert to dummies
data2 = create_dummies(data2, specific_cols)  # Saving dummy variables

# Ordinal encoding features with natural order
data2['education_of_employee'] = data2['education_of_employee'].replace({'High School': 1, "Bachelor's": 2, "Master's": 3,'Doctorate': 4})

data2.head()

	education_of_employee	has_job_experience	requires_job_training	no_of_employees	yr_of_estab	prevailing_wage	full_time_position	case_status	unit_of_wage_Hour	unit_of_wage_Month	unit_of_wage_Week	unit_of_wage_Year	region_of_employment_Island	region_of_employment_Midwest	region_of_employment_Northeast	region_of_employment_South	region_of_employment_West	continent_Africa	continent_Asia	continent_Europe	continent_North America	continent_Oceania	continent_South America
0	1	0	0	14513	2007	592.20290	1	0	1	0	0	0	0	0	0	0	1	0	1	0	0	0	0
1	3	1	0	2412	2002	83425.65000	1	1	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0
2	2	0	1	44444	2008	122996.86000	1	0	0	0	0	1	0	0	0	0	1	0	1	0	0	0	0
3	2	0	0	98	1897	83434.03000	1	0	0	0	0	1	0	0	0	0	1	0	1	0	0	0	0
4	3	1	0	1082	2005	149907.39000	1	1	0	0	0	1	0	0	0	1	0	1	0	0	0	0	0

Correlation Heatmap

# creating a list of numerical columns
num_cols = data2.select_dtypes(include=np.number).columns.tolist()

#plotting correlation heat map
plt.figure(figsize=(12, 7))
sns.heatmap(
    data2[num_cols].corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral")

plt.show()

Observations

• There is strong negative correlation between yearly and hourly wages, which is expected.
• There is a positive corrrelation between prevailing wage for the location and yearly wage unit. This may imply, employers tend to pay more when wage is calculated yearly, this is common in white collar jobs and mid to senior level positions. -There is a negative correlation between prevailing wages and hourly wage unit, as this implies the average pay for a job is likely lesser if its calculated hourly. This is common in blue collar jobs.
• There weak negative correlations for features among continent as well as those from region of employment. This is expected as they are dummy variables.

Case_status vs education of employee

plt.figure()
stacked_barplot(data,'education_of_employee', 'case_status')
plt.show();

case_status            Certified  Denied    All
education_of_employee                          
All                        17018    8462  25480
Bachelor's                  6367    3867  10234
High School                 1164    2256   3420
Master's                    7575    2059   9634
Doctorate                   1912     280   2192
------------------------------------------------------------------------------------------------------------------------

<Figure size 640x480 with 0 Axes>

• Visa approval rates steadily increases with employees level of education.This may imply Level of education a role in visa case status.

Case_status vs has job experience

plt.figure()
stacked_barplot(data,'has_job_experience','case_status' )
plt.show();

case_status         Certified  Denied    All
has_job_experience                          
All                     17018    8462  25480
N                        5994    4684  10678
Y                       11024    3778  14802
------------------------------------------------------------------------------------------------------------------------

<Figure size 640x480 with 0 Axes>

• Employees with previous job experience had about 70% chance of their visa getting approved when compared to those without job experience who had about a 50% chance, which is just next to luck.
• This findings may imply job experience play a role in the final visa case status.

Case_status vs requires job training

plt.figure()
stacked_barplot(data,'requires_job_training','case_status', )

plt.show();

case_status            Certified  Denied    All
requires_job_training                          
All                        17018    8462  25480
N                          15012    7513  22525
Y                           2006     949   2955
------------------------------------------------------------------------------------------------------------------------

<Figure size 640x480 with 0 Axes>

• There is no difference in visa approval rates between does who require job training and those who don't.

Case_status vs number of employees

plt.figure()
sns.boxplot(data = data,x = 'case_status' , y = 'no_of_employees')
plt.title('Boxplot of Number of Employees by Case Status')
plt.tight_layout()
plt.show();


sns.boxplot(data=data,x= 'case_status' ,y= 'no_of_employees',showfliers=False, palette="gist_rainbow")
plt.title('Boxplot of Number of Employees by Case Status (Excluding Outliers)')
plt.tight_layout()
plt.show();

• There is also no much difference for visa approval rates based on current employer staff size.

Case_status vs year company established

# Calculate the bins based on 20-year intervals
data2['year_established'] = pd.cut(data2['yr_of_estab'], bins=range(data2['yr_of_estab'].min(), data2['yr_of_estab'].max() + 21, 20), right=False)

# Plot the countplot with the specified bins
plt.figure()
sns.countplot(data=data2, x='year_established', hue='case_status')
# Modify legend labels
plt.legend(title='Case Status', labels=['Denied', 'Approved'])
plt.xticks(rotation=45)  # Rotate x-axis labels for better readability
plt.show()

• Employers whose companies were established from year 2000 till date formed the highest population of approvals and denials. This is understandable because majority of the companies sponssoring the visas for their employees were also established during this period.
• However, according to the different employer group based on year of company establishment, The ratio of approvals vs denials was almost 1:2. This may imply this factor may not play a major role in visa case status.

Case_status vs prevailing wage

plt.figure()
sns.boxplot(data, x = 'case_status', y ='prevailing_wage', )
plt.show();

There is not much dfference between visa application status for emplyees based of the prevailing wage of their jobs.

Case_status vs full time position

plt.figure()
stacked_barplot(data,'full_time_position','case_status' )
plt.show();

case_status         Certified  Denied    All
full_time_position                          
All                     17018    8462  25480
Y                       15163    7610  22773
N                        1855     852   2707
------------------------------------------------------------------------------------------------------------------------

<Figure size 640x480 with 0 Axes>

• The ratio of visa approvals and denials does not seem to be affected by if the employees job is full time or not.

Case_status vs unit of wage

plt.figure()
stacked_barplot(data,'unit_of_wage','case_status', )
plt.show();

case_status   Certified  Denied    All
unit_of_wage                          
All               17018    8462  25480
Year              16047    6915  22962
Hour                747    1410   2157
Week                169     103    272
Month                55      34     89
------------------------------------------------------------------------------------------------------------------------

<Figure size 640x480 with 0 Axes>

• There seem to be far lesser visa approvals for employees whose job unit wage is listed as hourly. This observation may be as a result of very few visa applications filing Hourly unit of wage.
• The unit of wage based on these findings may play a major role in the final case status.

Case_status vs region of employment

plt.figure()
stacked_barplot(data,'region_of_employment','case_status', )
plt.show();

case_status           Certified  Denied    All
region_of_employment                          
All                       17018    8462  25480
Northeast                  4526    2669   7195
West                       4100    2486   6586
South                      4913    2104   7017
Midwest                    3253    1054   4307
Island                      226     149    375
------------------------------------------------------------------------------------------------------------------------

<Figure size 640x480 with 0 Axes>

• All the regions seem to have the same visa approval rate, though the mid-west region seem to have slightly more visa approvals. This feature may have an impact on the final visa approval status.

Case_status vs continent

plt.figure()
stacked_barplot(data,'continent','case_status', )
plt.show();

case_status    Certified  Denied    All
continent                              
All                17018    8462  25480
Asia               11012    5849  16861
North America       2037    1255   3292
Europe              2957     775   3732
South America        493     359    852
Africa               397     154    551
Oceania              122      70    192
------------------------------------------------------------------------------------------------------------------------

<Figure size 640x480 with 0 Axes>

• Eventhough, Africa and Europe have slightly higher visa approval rates around 70% and 80% respectively. The approval rates for the other continents seem to be the same around 60%. This may imply this feature may not play much a role in the final visa case status.

Data Preprocessing

• Missing value treatment (if needed)
• Feature engineering
• Outlier detection and treatment (if needed)
• Preparing data for modeling
• Any other preprocessing steps (if needed)

df = data.copy() #Creating copy of data set from original dataset for preprocessing.

1. Missing value handling

Checking for missing value in data set.

missing_values = df.isnull().sum() #Checking for missing values
print(f'There are {missing_values.sum()} missing values') #printing total missing values

There are 0 missing values

There are no missing values to handle.

2. Feature engineering

No feature engineering needed.

3. Outlier detection and treatment.

Outlier Detection.

numerical_col = df.select_dtypes(include=np.number).columns.tolist() #Assigning numerical columns to a variable
plt.figure(figsize=(20, 30))

#Creating for loop to visualize columns with outliers from numerical columns
for i, variable in enumerate(numerical_col):
    plt.subplot(5, 4, i + 1)
    plt.boxplot(df[variable], whis=1.5)
    plt.tight_layout()
    plt.title(variable)

plt.show()

checking values of number of employees that were initially identified as negative and thus incorrect.

#Identifying rows with negative number of employees in company
negative_values_data = data[data['no_of_employees'] < 0]
negative_values_data

	case_id	continent	education_of_employee	has_job_experience	requires_job_training	no_of_employees	yr_of_estab	region_of_employment	prevailing_wage	unit_of_wage	full_time_position	case_status
245	EZYV246	Europe	Master's	N	N	-25	1980	Northeast	39452.99000	Year	Y	Certified
378	EZYV379	Asia	Bachelor's	N	Y	-11	2011	Northeast	32506.14000	Year	Y	Denied
832	EZYV833	South America	Master's	Y	N	-17	2002	South	129701.94000	Year	Y	Certified
2918	EZYV2919	Asia	Master's	Y	N	-26	2005	Midwest	112799.46000	Year	Y	Certified
6439	EZYV6440	Asia	Bachelor's	N	N	-14	2013	South	103.97000	Hour	Y	Denied
6634	EZYV6635	Asia	Bachelor's	Y	N	-26	1923	West	5247.32000	Year	Y	Denied
7224	EZYV7225	Europe	Doctorate	N	N	-25	1998	Midwest	141435.95000	Year	Y	Certified
7281	EZYV7282	Asia	High School	N	N	-14	2000	Midwest	58488.50000	Year	Y	Denied
7318	EZYV7319	Asia	Bachelor's	Y	Y	-26	2006	South	115005.61000	Year	Y	Certified
7761	EZYV7762	Asia	Master's	N	N	-11	2009	Midwest	38457.51000	Year	Y	Certified
9872	EZYV9873	Europe	Master's	Y	N	-26	1996	South	37397.05000	Year	Y	Certified
11493	EZYV11494	Asia	High School	Y	N	-14	1999	South	27599.35000	Year	Y	Denied
13471	EZYV13472	North America	Master's	N	N	-17	2003	Northeast	257.24130	Hour	Y	Denied
14022	EZYV14023	Asia	Bachelor's	N	Y	-11	1946	Northeast	108403.56000	Year	Y	Certified
14146	EZYV14147	Asia	Bachelor's	N	Y	-26	1954	West	81982.27000	Year	Y	Certified
14726	EZYV14727	Asia	Master's	N	N	-11	2000	Midwest	167851.80000	Year	Y	Certified
15600	EZYV15601	Asia	Bachelor's	N	N	-14	2014	South	24641.61000	Year	Y	Denied
15859	EZYV15860	Asia	High School	N	N	-11	1969	South	44640.60000	Year	Y	Denied
16157	EZYV16158	Asia	Master's	Y	N	-11	1994	South	62681.25000	Year	Y	Certified
16883	EZYV16884	North America	Bachelor's	Y	N	-26	1968	Northeast	168.15580	Hour	Y	Denied
17006	EZYV17007	Asia	Doctorate	Y	N	-11	1984	West	25753.51000	Year	Y	Denied
17655	EZYV17656	North America	Bachelor's	Y	N	-17	2007	Northeast	129753.18000	Year	Y	Denied
17844	EZYV17845	Asia	Bachelor's	N	N	-14	2012	West	29325.85000	Year	Y	Denied
17983	EZYV17984	Asia	Bachelor's	N	N	-26	2004	South	84359.98000	Year	Y	Denied
20815	EZYV20816	Asia	Bachelor's	N	Y	-17	1990	West	91897.57000	Year	Y	Certified
20984	EZYV20985	Europe	Doctorate	Y	N	-14	1989	Midwest	37012.80000	Year	Y	Certified
21255	EZYV21256	North America	High School	N	N	-25	1987	South	99405.47000	Year	N	Denied
21760	EZYV21761	Asia	Bachelor's	Y	N	-25	2000	West	100463.58000	Year	Y	Certified
21944	EZYV21945	Africa	Master's	Y	N	-25	1977	Midwest	79150.51000	Year	Y	Certified
22084	EZYV22085	North America	Bachelor's	Y	N	-14	1980	West	691.06090	Hour	Y	Denied
22388	EZYV22389	Asia	Master's	Y	N	-14	1986	South	17893.11000	Year	Y	Certified
23186	EZYV23187	Asia	Master's	N	Y	-11	2007	Midwest	120195.35000	Year	Y	Certified
23476	EZYV23477	Europe	Master's	Y	N	-11	2000	West	95072.75000	Year	Y	Denied

Treating outliers

From the data overview it was observed that in some applications filed, the total number of employees in the applying company had negative values which is actually impossible. These negative values may impact the performance of my models negatively, since they could include biase to the model.The negatve values are actually outlier, Hence this will be the only outlier I will be treating. There are quite a few other numerical features with outliers, but they will not be treated because they are proper values.

# User defined functions to treat outliers by flooring and capping


def treat_outliers_lower(df, col):
    """
    Treats outliers in a variable

    df: dataframe
    col: dataframe column
    """
    Q1 = df[col].quantile(0.25)  # 25th quantile
    Q3 = df[col].quantile(0.75)  # 75th quantile
    IQR = Q3 - Q1
    Lower_Whisker = Q1 - 1.5 * IQR

    # Replace the lower whisker with zero if it's negative
    Lower_Whisker = max(0, Lower_Whisker)

    # all the values smaller than Lower_Whisker will be assigned the value of Lower_Whisker

    df[col] = np.clip(df[col], Lower_Whisker, np.inf)

    return df

#treating outliers with user defined function.
df = treat_outliers_lower(df, 'no_of_employees')

#Identifying rows with negative number of employees in company
negative_values_df = df[df['no_of_employees'] < 0]
negative_values_df

	case_id	continent	education_of_employee	has_job_experience	requires_job_training	no_of_employees	yr_of_estab	region_of_employment	prevailing_wage	unit_of_wage	full_time_position	case_status

There are no more negative values in number of employees

4. Preparing data for modelling.

• As identified earlier, there is significant imbalance in the ditribution of the target classes, since we are dealing with a classification problem, it is important to use stratified sampling. This will help preserve the relative class frequencies.
• This is done using the 'stratify' parameter in the training_test_split function.

# Converting Boolean variables to binary coding
df['case_status'] = df['case_status'].replace({'Certified': 1, 'Denied': 0})
df['full_time_position'] = df['full_time_position'].replace({'Y': 1, 'N': 0})
df['requires_job_training'] = df['requires_job_training'].replace({'Y': 1, 'N': 0})
df['has_job_experience'] = df['has_job_experience'].replace({'Y': 1, 'N': 0})

# Ordinal encoding features with natural order
df['education_of_employee'] = df['education_of_employee'].replace({'High School': 1, "Bachelor's": 2, "Master's": 3,'Doctorate': 4})

df = X = df.drop(['case_id'],axis=1)

# One-hot encoding non-ordinal features
def create_dummies(df, specific_cols):
    """
    Creates dummy variables for specific columns in a pandas DataFrame.

    Args:
        data: The pandas DataFrame.
        specific_cols (list): A list of column names to convert to dummy variables.

    Returns:
        pandas.DataFrame: The DataFrame with new dummy variables.
    """

    # Create dummy variables for the specified columns
    dummies = pd.get_dummies(df[specific_cols], drop_first=False)

    # Drop the original columns from the DataFrame
    df = df.drop(columns=specific_cols)

    # Combine original data without the specified columns with dummy variables
    return pd.concat([df, dummies], axis=1)


specific_cols = ['unit_of_wage', 'region_of_employment', 'continent']  # Columns to convert to dummies
df = create_dummies(df, specific_cols)  # Saving dummy variables

X = df.drop(['case_status'],axis=1)
y = df['case_status']

X = pd.get_dummies(X,drop_first=False)

# Splitting data into training and test set:
X_train, X_test, y_train, y_test =train_test_split(X, y, test_size=0.3, random_state=1,stratify=y)
print(X_train.shape, X_test.shape)

(17836, 22) (7644, 22)

y.value_counts(1)

case_status
1   0.66790
0   0.33210
Name: proportion, dtype: float64

y_test.value_counts(1)

case_status
1   0.66784
0   0.33216
Name: proportion, dtype: float64

df.head()

	education_of_employee	has_job_experience	requires_job_training	no_of_employees	yr_of_estab	prevailing_wage	full_time_position	case_status	unit_of_wage_Hour	unit_of_wage_Month	unit_of_wage_Week	unit_of_wage_Year	region_of_employment_Island	region_of_employment_Midwest	region_of_employment_Northeast	region_of_employment_South	region_of_employment_West	continent_Africa	continent_Asia	continent_Europe	continent_North America	continent_Oceania	continent_South America
0	1	0	0	14513	2007	592.20290	1	0	True	False	False	False	False	False	False	False	True	False	True	False	False	False	False
1	3	1	0	2412	2002	83425.65000	1	1	False	False	False	True	False	False	True	False	False	False	True	False	False	False	False
2	2	0	1	44444	2008	122996.86000	1	0	False	False	False	True	False	False	False	False	True	False	True	False	False	False	False
3	2	0	0	98	1897	83434.03000	1	0	False	False	False	True	False	False	False	False	True	False	True	False	False	False	False
4	3	1	0	1082	2005	149907.39000	1	1	False	False	False	True	False	False	False	True	False	True	False	False	False	False	False

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 25480 entries, 0 to 25479
Data columns (total 23 columns):
 #   Column                          Non-Null Count  Dtype  
---  ------                          --------------  -----  
 0   education_of_employee           25480 non-null  int64  
 1   has_job_experience              25480 non-null  int64  
 2   requires_job_training           25480 non-null  int64  
 3   no_of_employees                 25480 non-null  int64  
 4   yr_of_estab                     25480 non-null  int64  
 5   prevailing_wage                 25480 non-null  float64
 6   full_time_position              25480 non-null  int64  
 7   case_status                     25480 non-null  int64  
 8   unit_of_wage_Hour               25480 non-null  bool   
 9   unit_of_wage_Month              25480 non-null  bool   
 10  unit_of_wage_Week               25480 non-null  bool   
 11  unit_of_wage_Year               25480 non-null  bool   
 12  region_of_employment_Island     25480 non-null  bool   
 13  region_of_employment_Midwest    25480 non-null  bool   
 14  region_of_employment_Northeast  25480 non-null  bool   
 15  region_of_employment_South      25480 non-null  bool   
 16  region_of_employment_West       25480 non-null  bool   
 17  continent_Africa                25480 non-null  bool   
 18  continent_Asia                  25480 non-null  bool   
 19  continent_Europe                25480 non-null  bool   
 20  continent_North America         25480 non-null  bool   
 21  continent_Oceania               25480 non-null  bool   
 22  continent_South America         25480 non-null  bool   
dtypes: bool(15), float64(1), int64(7)
memory usage: 1.9 MB

# defining a function to compute different metrics to check performance of a classification model built using sklearn
def model_performance_classification_sklearn(model, predictors, target):
    """
    Function to compute different metrics to check classification model performance

    model: classifier
    predictors: independent variables
    target: dependent variable
    """

    # predicting using the independent variables
    pred = model.predict(predictors)

    acc = accuracy_score(target, pred)  # to compute Accuracy
    recall = recall_score(target, pred)  # to compute Recall
    precision = precision_score(target, pred)  # to compute Precision
    f1 = f1_score(target, pred)  # to compute F1-score

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {
            "Accuracy": acc,
            "Recall": recall,
            "Precision": precision,
            "F1": f1,
        },
        index=[0],
    )

    return df_perf

def confusion_matrix_sklearn(model, predictors, target):
    """
    To plot the confusion_matrix with percentages

    model: classifier
    predictors: independent variables
    target: dependent variable
    """
    y_pred = model.predict(predictors)
    cm = confusion_matrix(target, y_pred)
    labels = np.asarray(
        [
            ["{0:0.0f}".format(item) + "\n{0:.2%}".format(item / cm.flatten().sum())]
            for item in cm.flatten()
        ]
    ).reshape(2, 2)

    plt.figure(figsize=(6, 4))
    sns.heatmap(cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")

EDA

• It is a good idea to explore the data once again after manipulating it.

custom function

#Defining custom variable t plot combine histogram and box plot
def histogram_boxplot1(df, feature, figsize=(15, 10), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (15,10))
    kde: whether to show the density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=df, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a triangle will indicate the mean value of the column
    sns.histplot(
        data=df, x=feature, kde=kde, ax=ax_hist2, bins=bins
    ) if bins else sns.histplot(
        data=df, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        df[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        df[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram

# function to create labeled barplots


def labeled_barplot1(data, feature, perc=True, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(df[feature])  # length of the column
    count = df[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 2, 6))
    else:
        plt.figure(figsize=(n + 2, 6))

    plt.xticks(rotation=90, fontsize=15)
    ax = sns.countplot(
        data=df,
        x=feature,
        palette="Paired",
        order=df[feature].value_counts().index[:n],
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot

#Defining function to plot stacked barplot
def stacked_barplot1(data, predictor, target, legend_labels=None, xlabel=None):
    """
    Print the category counts and plot a stacked bar chart

    data: dataframe
    predictor: independent variable
    target: target variable
    legend_labels: list of legend labels (optional)
    xlabel: label for the x-axis (optional)
    """
    count = data[predictor].nunique()
    sorter = data[target].value_counts().index[-1]
    tab1 = pd.crosstab(data[predictor], data[target], margins=True).sort_values(
        by=sorter, ascending=False
    )
    print(tab1)
    print("-" * 120)
    tab = pd.crosstab(data[predictor], data[target], normalize="index").sort_values(
        by=sorter, ascending=False
    )
    ax = tab.plot(kind="bar", stacked=True, figsize=(count + 5, 5))

    # Customize legend if legend_labels provided
    if legend_labels:
        plt.legend(legend_labels, loc="upper left", bbox_to_anchor=(1, 1))

    # Set X label if xlabel provided
    if xlabel:
        plt.xlabel(xlabel)

    plt.show()

# function to plot distributions wrt target


def distribution_plot_wrt_target1(data, predictor, target):

    fig, axs = plt.subplots(2, 2, figsize=(12, 10))

    target_uniq = df[target].unique()

    axs[0, 0].set_title("Distribution of target for target=" + str(target_uniq[0]))
    sns.histplot(
        data=data1[df[target] == target_uniq[0]],
        x=predictor,
        kde=True,
        ax=axs[0, 0],
        color="teal",
        stat="density",
    )

    axs[0, 1].set_title("Distribution of target for target=" + str(target_uniq[1]))
    sns.histplot(
        data=df[df[target] == target_uniq[1]],
        x=predictor,
        kde=True,
        ax=axs[0, 1],
        color="orange",
        stat="density",
    )

    axs[1, 0].set_title("Boxplot w.r.t target")
    sns.boxplot(data=df, x=target, y=predictor, ax=axs[1, 0], palette="gist_rainbow")

    axs[1, 1].set_title("Boxplot (without outliers) w.r.t target")
    sns.boxplot(
        data=df,
        x=target,
        y=predictor,
        ax=axs[1, 1],
        showfliers=False,
        palette="gist_rainbow",
    )

    plt.tight_lay

Univariate analysis

# Assign variable to plot univariate chart

continuous_cols = df.select_dtypes(include=['int', 'float', 'bool'])  # Select integer, float, and boolean datatypes

# Loop through continuous columns
for col in continuous_cols.columns:
    plt.figure()  # Create a new figure for each plot
    histogram_boxplot1(df, col)  # Pass the column name (feature)

    # Set the plot title using the column name
    plt.title(col)
    plt.show()
  # Select integer, float, and boolean datatypes

# Loop through continuous columns
for col in continuous_cols.columns:
    plt.figure()  # Create a new figure for each plot
    histogram_boxplot1(df, col)  # Pass the column name (feature)

    # Set the plot title using the column name
    plt.title(col)
    plt.show()

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Categorical variable

# Check for categorical data types
categorical_cols = df.select_dtypes(include=['category'])  # Select category columns (categorical data)

# Check if there are any categorical columns
if not categorical_cols.empty:
    # Loop through categorical columns
    for col in categorical_cols.columns:
       plt.figure()  # Create a new figure for each plot
       labeled_barplot1(df, col)  # Plot bar chart of categorical variables
       plt.show()
else:
    print("No columns with categorical data found.")

No columns with categorical data found.

• There are no more categorical variables after processing.

Bivariate analysis

Correlation heatmap.

df1 = df.copy()

df1.head()

	education_of_employee	has_job_experience	requires_job_training	no_of_employees	yr_of_estab	prevailing_wage	full_time_position	case_status	unit_of_wage_Hour	unit_of_wage_Month	unit_of_wage_Week	unit_of_wage_Year	region_of_employment_Island	region_of_employment_Midwest	region_of_employment_Northeast	region_of_employment_South	region_of_employment_West	continent_Africa	continent_Asia	continent_Europe	continent_North America	continent_Oceania	continent_South America
0	1	0	0	14513	2007	592.20290	1	0	True	False	False	False	False	False	False	False	True	False	True	False	False	False	False
1	3	1	0	2412	2002	83425.65000	1	1	False	False	False	True	False	False	True	False	False	False	True	False	False	False	False
2	2	0	1	44444	2008	122996.86000	1	0	False	False	False	True	False	False	False	False	True	False	True	False	False	False	False
3	2	0	0	98	1897	83434.03000	1	0	False	False	False	True	False	False	False	False	True	False	True	False	False	False	False
4	3	1	0	1082	2005	149907.39000	1	1	False	False	False	True	False	False	False	True	False	True	False	False	False	False	False

# Select numerical columns (integer and float) from the DataFrame
continuous_cols = df1.select_dtypes(include=['int', 'float'])

# Convert boolean columns to numerical values (0 or 1)
boolean_cols = df1.select_dtypes(include=['bool'])
for col in boolean_cols.columns:
    df1[col] = df1[col].astype(int)

# Plot correlation heatmap
plt.figure(figsize=(12, 7))
sns.heatmap(
    df1.corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral")
plt.show()

Case status vs Employee educational level.

plt.figure()
stacked_barplot1(df1, 'education_of_employee', 'case_status', legend_labels=['Denied', 'Certified'], xlabel='Education Level')
plt.show()

case_status               0      1    All
education_of_employee                    
All                    8462  17018  25480
2                      3867   6367  10234
1                      2256   1164   3420
3                      2059   7575   9634
4                       280   1912   2192
------------------------------------------------------------------------------------------------------------------------

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Case status vs number of employees

plt.figure()
sns.boxplot(data = df,x = 'case_status' , y = 'no_of_employees')
plt.title('Boxplot of Number of Employees by Case Status')
plt.tight_layout()
plt.show();


sns.boxplot(data=df,x= 'case_status' ,y= 'no_of_employees',showfliers=False, palette="gist_rainbow")
plt.title('Boxplot of Number of Employees by Case Status (Excluding Outliers)')
plt.tight_layout()
plt.show();

Case status vs job experience

plt.figure()
stacked_barplot1(df,'has_job_experience','case_status' )
plt.show();

case_status            0      1    All
has_job_experience                    
All                 8462  17018  25480
0                   4684   5994  10678
1                   3778  11024  14802
------------------------------------------------------------------------------------------------------------------------

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Building bagging and boosting models

• I will be building multiple bagging and boosting models as well as a decision tree model to get the best prediction for my data set.
• The metrics I will be using to measure the performance of my models will be the f1-score.
• The model will be futher tuned using multiple parameter values using GridSearch function.
• Predictions will be made based on the model with the best test performance, as well as the ability of the model to generalize on unseen data.

Decision Tree model(base)

Since the classes are unbalanced, I will be setting class weights to account for this.

dtree = DecisionTreeClassifier(criterion='gini',class_weight={0:0.40,1:0.60},random_state=1) #assigning the base classifier to the variable created for this model

dtree.fit(X_train, y_train) #training the decision tree model

DecisionTreeClassifier(class_weight={0: 0.4, 1: 0.6}, random_state=1)

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confusion_matrix_sklearn(dtree, X_train, y_train)

dtree_model_train_perf=model_performance_classification_sklearn(dtree, X_train, y_train)#evaluating performance on tain data
print("Training performance \n",dtree_model_train_perf)

Training performance 
    Accuracy  Recall  Precision      F1
0   1.00000 1.00000    1.00000 1.00000

confusion_matrix_sklearn(dtree, X_test, y_test)

dtree_model_test_perf=model_performance_classification_sklearn(dtree, X_test, y_test)#evaluating performance on test data
print("Testing performance \n",dtree_model_test_perf)

Testing performance 
    Accuracy  Recall  Precision      F1
0   0.66130 0.74535    0.74696 0.74615

• The decision model with class weight does perform well in f1_score, however, the model overfits and performance drops with unseen data.
• More tuning will be done to improve performance.

Bagging models

• I will be creating bagging models with and without class weight check for any improvements between them.

Bagging Clasifier

bagging = BaggingClassifier(random_state=1) #assigning baggingclassifier
bagging.fit(X_train,y_train)#training model

BaggingClassifier(random_state=1)

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confusion_matrix_sklearn(bagging, X_train, y_train)

bagging_model_train_perf=model_performance_classification_sklearn(bagging, X_train, y_train)#evaluating performance on train data
print("Training performance \n",bagging_model_train_perf)

Training performance 
    Accuracy  Recall  Precision      F1
0   0.98312 0.98372    0.99095 0.98732

confusion_matrix_sklearn(bagging, X_test, y_test)

bagging_model_test_perf=model_performance_classification_sklearn(bagging, X_test, y_test)#evaluating performance on test data
print("Testing performance \n",bagging_model_test_perf)

Testing performance 
    Accuracy  Recall  Precision      F1
0   0.69780 0.77160    0.77494 0.77326

BAGGING CLASSIFIER WITH WEIGHTED DECISION TREE

bagging_wt = BaggingClassifier(base_estimator=DecisionTreeClassifier(criterion='gini',class_weight={0:0.40,1:0.60},random_state=1),random_state=1)
bagging_wt.fit(X_train,y_train)

BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight={0: 0.4,
                                                                      1: 0.6},
                                                        random_state=1),
                  random_state=1)

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confusion_matrix_sklearn(bagging_wt,X_train,y_train)

bagging_wt_model_train_perf=model_performance_classification_sklearn(bagging_wt,X_train,y_train)
print("Training performance \n",bagging_wt_model_train_perf)

Training performance 
    Accuracy  Recall  Precision      F1
0   0.98458 0.98539    0.99147 0.98842

confusion_matrix_sklearn(bagging_wt,X_test,y_test)

bagging_wt_model_test_perf=model_performance_classification_sklearn(bagging_wt, X_test, y_test)
print("Testing performance \n",bagging_wt_model_test_perf)

Testing performance 
    Accuracy  Recall  Precision      F1
0   0.69008 0.76631    0.76887 0.76759

• Both weighted and default weighted bagging classifier models perform well, however,they are both overfitted.
• The default weighted model here even performed slightly better with f1_score than the weighted model.

Random Forest model

rf = RandomForestClassifier(oob_score=True,random_state=1) #Assigning randomforest classifier
rf.fit(X_train,y_train) #training model

RandomForestClassifier(oob_score=True, random_state=1)

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confusion_matrix_sklearn(rf,X_train,y_train)

rf_model_train_perf=model_performance_classification_sklearn(rf,X_train,y_train)#evaluating performance on train data
print("Training performance \n",rf_model_train_perf)

Training performance 
    Accuracy  Recall  Precision      F1
0   1.00000 1.00000    1.00000 1.00000

# Access the OOB predictions
oob_predictions = rf.predict(X_train)

# Compute f1-score using OOB predictions and true labels
oob_f1_score = f1_score(y_train, oob_predictions)

print("OOB F1 Score:", oob_f1_score)

OOB F1 Score: 1.0

confusion_matrix_sklearn(rf,X_test,y_test)

rf_model_test_perf=model_performance_classification_sklearn(rf,X_test,y_test)#evaluating performance on test data
print("Testing performance \n",rf_model_test_perf)

Testing performance 
    Accuracy  Recall  Precision      F1
0   0.72305 0.83565    0.76948 0.80120

RANDOM FOREST WITH CLASS WEIGHTS

rf_wt = RandomForestClassifier(oob_score=True,class_weight={0:0.40,1:0.60}, random_state=1)
rf_wt.fit(X_train,y_train)

RandomForestClassifier(class_weight={0: 0.4, 1: 0.6}, oob_score=True,
                       random_state=1)

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confusion_matrix_sklearn(rf_wt, X_train,y_train)

rf_wt_model_train_perf=model_performance_classification_sklearn(rf_wt, X_train,y_train)
print("Training performance \n",rf_wt_model_train_perf)

Training performance 
    Accuracy  Recall  Precision      F1
0   1.00000 1.00000    1.00000 1.00000

confusion_matrix_sklearn(rf_wt, X_test,y_test)

rf_wt_model_test_perf=model_performance_classification_sklearn(rf_wt, X_test,y_test)
print("Testing performance \n",rf_wt_model_test_perf)

Testing performance 
    Accuracy  Recall  Precision      F1
0   0.72083 0.82997    0.76994 0.79883

• Here, the default weighted RandomForest model still performs slightly better than the weighted model.
• Both model perform even marginally better than the BaggingClassifier models.
• However, they still suffer from a lot of over-fitting and needs further tuning.

Boosting models

GBM Model

gb=GradientBoostingClassifier(random_state=1)
gb.fit(X_train,y_train)

GradientBoostingClassifier(random_state=1)

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confusion_matrix_sklearn(gb, X_train,y_train)

gb_model_train_perf = model_performance_classification_sklearn(gb, X_train,y_train)
print("Training performance \n",gb_model_train_perf)

Training performance 
    Accuracy  Recall  Precision      F1
0   0.75791 0.88332    0.78232 0.82976

confusion_matrix_sklearn(gb, X_test,y_test)

gb_model_test_perf = model_performance_classification_sklearn(gb, X_test, y_test)
print("Testing performance \n",gb_model_test_perf)

Testing performance 
    Accuracy  Recall  Precision      F1
0   0.74555 0.87640    0.77298 0.82144

• This is the best performing model so far.
• It is also normally fitted, hence will generalize well on unseen data.

AdaBoost Model

ab_classifier=AdaBoostClassifier(random_state=1)
ab_classifier.fit(X_train,y_train)

AdaBoostClassifier(random_state=1)

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confusion_matrix_sklearn(ab_classifier, X_train,y_train)

ab_classifier_model_train_perf = model_performance_classification_sklearn(ab_classifier, X_train,y_train)
print("Training performance \n",ab_classifier_model_train_perf)

Training performance 
    Accuracy  Recall  Precision      F1
0   0.73710 0.88945    0.75859 0.81882

confusion_matrix_sklearn(ab_classifier, X_test,y_test)

ab_classifier_model_test_perf = model_performance_classification_sklearn(ab_classifier, X_test,y_test)
print("Testing performance \n",ab_classifier_model_test_perf)

Testing performance 
    Accuracy  Recall  Precision      F1
0   0.73299 0.88521    0.75644 0.81578

• This model also performs very well.
• It is normally fitted and will generalize well on unseen data.
• Though this model far performs better than the bagging models and the decision tree model, the earlier Gbm model still performs slightly better than it in F1_score metric.

XG BOOST MODEL

xgb_classifier=XGBClassifier(random_state=1, verbosity = 0)
xgb_classifier.fit(X_train,y_train)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=None, n_jobs=None,
              num_parallel_tree=None, random_state=1, ...)

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confusion_matrix_sklearn(xgb_classifier, X_train,y_train)

xgb_classifer_model_train_perf =  model_performance_classification_sklearn(xgb_classifier, X_train, y_train)
print("Training performance \n",xgb_classifer_model_train_perf )

Training performance 
    Accuracy  Recall  Precision      F1
0   0.84587 0.93184    0.85143 0.88982

confusion_matrix_sklearn(xgb_classifier, X_test,y_test)

xgb_classifer_model_test_perf =  model_performance_classification_sklearn(xgb_classifier, X_test, y_test)
print("Test performance \n",xgb_classifer_model_test_perf )

Test performance 
    Accuracy  Recall  Precision      F1
0   0.72828 0.85524    0.76543 0.80785

• This model performs very well, and even have test F1_score that is close to the earlier GBM and Ada boost models.
• However , this model slightly overfits and may need further tuning.

Notes for hypertuning

Note

1. Sample parameter grids have been provided to do necessary hyperparameter tuning. These sample grids are expected to provide a balance between model performance improvement and execution time. One can extend/reduce the parameter grid based on execution time and system configuration.
   • Please note that if the parameter grid is extended to improve the model performance further, the execution time will increase

• For Gradient Boosting:

param_grid = {
    "init": [AdaBoostClassifier(random_state=1),DecisionTreeClassifier(random_state=1)],
    "n_estimators": np.arange(50,110,25),
    "learning_rate": [0.01,0.1,0.05],
    "subsample":[0.7,0.9],
    "max_features":[0.5,0.7,1],
}

• For Adaboost:

param_grid = {
    "n_estimators": np.arange(50,110,25),
    "learning_rate": [0.01,0.1,0.05],
    "base_estimator": [
        DecisionTreeClassifier(max_depth=2, random_state=1),
        DecisionTreeClassifier(max_depth=3, random_state=1),
    ],
}

• For Bagging Classifier:

param_grid = {
    'max_samples': [0.8,0.9,1],
    'max_features': [0.7,0.8,0.9],
    'n_estimators' : [30,50,70],
}

• For Random Forest:

param_grid = {
    "n_estimators": [50,110,25],
    "min_samples_leaf": np.arange(1, 4),
    "max_features": [np.arange(0.3, 0.6, 0.1),'sqrt'],
    "max_samples": np.arange(0.4, 0.7, 0.1)
}

• For Decision Trees:

param_grid = {
    'max_depth': np.arange(2,6),
    'min_samples_leaf': [1, 4, 7],
    'max_leaf_nodes' : [10, 15],
    'min_impurity_decrease': [0.0001,0.001]
}

• For XGBoost:

param_grid={'n_estimators':np.arange(50,110,25),
            'scale_pos_weight':[1,2,5],
            'learning_rate':[0.01,0.1,0.05],
            'gamma':[1,3],
            'subsample':[0.7,0.9]
}

Will tuning the hyperparameters improve the model performance?

• So far, the basic Adaboost and GBM models with default parameter performed the best and normally fitted. Normally, this might imply there is no further need for tuning these two models. But, I will be tuning them purely for acedemic purposes.

• The models will have to models,eventhough, perform well. Needs to be tuned to improve their ability to generalize on unseen data.

• I will be using the GridSearch function to tune the Bagging and boosting model, as well as, tuning the decision tree models.

GBM tuning

# Choose the type of classifier.
gb_tuned = GradientBoostingClassifier(random_state=1)

# Grid of parameters to choose from
param_grid  = {
    "n_estimators": np.arange(50, 200, 25),
    "learning_rate": [0.01, 0.05,0.1, 0.8],
    "subsample": [0.7, 0.9],
    "max_depth": [2,3],
}

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.f1_score)

# Run the grid search
grid_obj = GridSearchCV(gb_tuned, param_grid, scoring=scorer,cv=5)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
gb_tuned = grid_obj.best_estimator_

# Fit the best algorithm to the data.
gb_tuned.fit(X_train, y_train)

GradientBoostingClassifier(learning_rate=0.05, n_estimators=75, random_state=1,
                           subsample=0.7)

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confusion_matrix_sklearn(gb_tuned, X_train,y_train)

gb_model_tuned_train_perf = model_performance_classification_sklearn(gb_tuned, X_train,y_train)
print("Training performance \n",gb_model_tuned_train_perf)

Training performance 
    Accuracy  Recall  Precision      F1
0   0.74983 0.89801    0.76716 0.82744

confusion_matrix_sklearn(gb_tuned, X_test,y_test)

gb_model_tuned_test_perf = model_performance_classification_sklearn(gb_tuned, X_test, y_test)
print("Testing performance \n",gb_model_tuned_test_perf)

Testing performance 
    Accuracy  Recall  Precision      F1
0   0.74241 0.89618    0.76072 0.82292

# importance of features in the tree building ( The importance of a feature is computed as the
#(normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance )

print(pd.DataFrame(gb_tuned.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))

                                   Imp
education_of_employee          0.50100
has_job_experience             0.16214
prevailing_wage                0.06961
unit_of_wage_Hour              0.06483
continent_Europe               0.05783
region_of_employment_Midwest   0.03336
unit_of_wage_Year              0.02089
region_of_employment_South     0.02024
continent_North America        0.01625
region_of_employment_West      0.01380
continent_Asia                 0.01150
region_of_employment_Northeast 0.00743
yr_of_estab                    0.00649
no_of_employees                0.00514
full_time_position             0.00390
continent_South America        0.00361
requires_job_training          0.00113
continent_Africa               0.00046
region_of_employment_Island    0.00021
unit_of_wage_Month             0.00011
unit_of_wage_Week              0.00007
continent_Oceania              0.00000

feature_names7 = X_train.columns
importances = gb_tuned.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12,12))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [feature_names7[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()

• As espected no much improved model in this tuned model from the untuned model based on the f1_score.
• However, there is seem to be a 1% improvement in the recall metrics.
• model is still very well able to generalize on seen data.

AdaBoost Tuning

# Create a one-step decision tree (decision stump)
decision_stump = DecisionTreeClassifier(max_depth=1, random_state=1)

# Choose the type of classifier.
ab_tuned = AdaBoostClassifier(base_estimator=decision_stump, random_state=1)

# Grid of parameters to choose from
parameters = {
    "n_estimators": np.arange(10, 200, 10),
    "learning_rate": [1, 0.1, 0.5, 0.8, 0.01],
    "algorithm": ['SAMME', 'SAMME.R'],
}

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.f1_score)

# Run the grid search
grid_obj = GridSearchCV(ab_tuned, parameters, scoring=scorer, cv=5)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
ab_tuned = grid_obj.best_estimator_

# Fit the best algorithm to the data.
ab_tuned.fit(X_train, y_train)

AdaBoostClassifier(algorithm='SAMME',
                   base_estimator=DecisionTreeClassifier(max_depth=1,
                                                         random_state=1),
                   learning_rate=0.8, n_estimators=20, random_state=1)

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confusion_matrix_sklearn(ab_tuned, X_train,y_train)

ab_model_tuned_train_perf = model_performance_classification_sklearn(ab_tuned, X_train,y_train)
print("Training performance \n",ab_model_tuned_train_perf)

Training performance 
    Accuracy  Recall  Precision      F1
0   0.73688 0.89499    0.75596 0.81962

confusion_matrix_sklearn(ab_tuned, X_test,y_test)

ab_model_tuned_test_perf = model_performance_classification_sklearn(ab_tuned, X_test, y_test)
print("Testing performance \n",ab_model_tuned_test_perf)

Testing performance 
    Accuracy  Recall  Precision      F1
0   0.73155 0.89246    0.75194 0.81619

# importance of features in the tree building ( The importance of a feature is computed as the
#(normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance )

print(pd.DataFrame(ab_tuned.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))

                                   Imp
education_of_employee          0.43232
has_job_experience             0.12292
continent_Europe               0.11288
unit_of_wage_Hour              0.10701
region_of_employment_Midwest   0.09952
unit_of_wage_Year              0.05881
region_of_employment_West      0.03569
region_of_employment_South     0.03086
region_of_employment_Northeast 0.00000
continent_Oceania              0.00000
continent_North America        0.00000
continent_Asia                 0.00000
continent_Africa               0.00000
region_of_employment_Island    0.00000
unit_of_wage_Week              0.00000
unit_of_wage_Month             0.00000
full_time_position             0.00000
prevailing_wage                0.00000
yr_of_estab                    0.00000
no_of_employees                0.00000
requires_job_training          0.00000
continent_South America        0.00000

feature_names6 = X_train.columns
importances = ab_tuned.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12,12))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [feature_names6[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()

• There is no much improvement tuning the Ada boost model.
• model is still able to genearalize well on unseen data.
• Though f1_score performance is still slightly not as good as the GBM tuned model.

XG BOOSTING MODEL TUNED

# Choose the type of classifier.
xgb_tuned = XGBClassifier(random_state=1, verbosity = 0)

# Grid of parameters to choose from
parameters={'n_estimators':np.arange(50,110,25),
            'scale_pos_weight':[1,2,5],
            'learning_rate':[0.01,0.1,0.05,0.7],
            'gamma':[1,3],
            'subsample':[0.7,0.9]
}
# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.f1_score)

# Run the grid search
grid_obj = GridSearchCV(xgb_tuned, parameters, scoring=scorer,cv=5)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the clf to the best combination of parameters
xgb_tuned = grid_obj.best_estimator_

# Fit the best algorithm to the data.
xgb_tuned.fit(X_train, y_train)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, device=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=3, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=0.05, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              multi_strategy=None, n_estimators=50, n_jobs=None,
              num_parallel_tree=None, random_state=1, ...)

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confusion_matrix_sklearn(xgb_tuned, X_train,y_train)

xgb_tuned_model_train_perf = model_performance_classification_sklearn(xgb_tuned, X_train,y_train)
print("Training performance \n",xgb_tuned_model_train_perf)

Training performance 
    Accuracy  Recall  Precision      F1
0   0.76233 0.88903    0.78405 0.83325

confusion_matrix_sklearn(xgb_tuned, X_test,y_test)

xgb_tuned_model_test_perf = model_performance_classification_sklearn(xgb_tuned, X_test, y_test)
print("Testing performance \n",xgb_tuned_model_test_perf)

Testing performance 
    Accuracy  Recall  Precision      F1
0   0.74451 0.87796    0.77116 0.82110

# importance of features in the tree building ( The importance of a feature is computed as the
#(normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance )

print(pd.DataFrame(xgb_tuned.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))

                                   Imp
education_of_employee          0.24798
unit_of_wage_Hour              0.22538
has_job_experience             0.13301
continent_Europe               0.04712
unit_of_wage_Year              0.04645
region_of_employment_Midwest   0.03219
region_of_employment_South     0.02763
region_of_employment_Northeast 0.02737
full_time_position             0.02356
region_of_employment_West      0.02272
continent_Asia                 0.01999
continent_North America        0.01940
requires_job_training          0.01890
prevailing_wage                0.01885
continent_South America        0.01696
continent_Africa               0.01267
region_of_employment_Island    0.01252
yr_of_estab                    0.01173
no_of_employees                0.00985
unit_of_wage_Week              0.00947
continent_Oceania              0.00920
unit_of_wage_Month             0.00705

feature_names = X_train.columns
importances = xgb_tuned.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12,12))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()

• The tuned XGB model still performs well and overfitting has also reduced to 1%,though this still not as good as the GBM Ada boost models which do not have any overfitting.

Random Forest Tuning

# Choose the type of classifier.
rf_classifier_tuned = RandomForestClassifier(oob_score=True,random_state=1)

# Grid of parameters to choose from
parameters = {
    "n_estimators": [25,300,25],
    "min_samples_leaf": np.arange(1, 4),
    "max_features": [np.arange(0.3, 0.9, 0.1),'sqrt'],
    "max_samples": np.arange(0.4, 0.7, 0.1)
}

# Type of scoring used to compare parameter combinations
scorer = metrics.make_scorer(metrics.f1_score)

# Run the grid search
grid_obj = GridSearchCV(rf_classifier_tuned, parameters, scoring=scorer,cv=5)
grid_obj = grid_obj.fit(X_train, y_train)

# Set the rf_classifier_tuned to the best combination of parameters
rf_classifier_tuned= grid_obj.best_estimator_

# Fit the best algorithm to the data.
rf_classifier_tuned.fit(X_train, y_train)

RandomForestClassifier(max_samples=0.4, min_samples_leaf=3, n_estimators=300,
                       oob_score=True, random_state=1)

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confusion_matrix_sklearn(rf_classifier_tuned, X_train,y_train)

rf_classifier_tuned_model_train_perf=model_performance_classification_sklearn(rf_classifier_tuned, X_train,y_train)
print("Training performance \n",rf_classifier_tuned_model_train_perf)

Training performance 
    Accuracy  Recall  Precision      F1
0   0.79160 0.90859    0.80464 0.85346

# Access the OOB predictions
oob_predictions_rf_tuned = rf_classifier_tuned.predict(X_train)

# Compute f1-score using OOB predictions and true labels
oob_f1_score_rf_tuned = f1_score(y_train, oob_predictions_rf_tuned)

print("OOB F1 Score:", oob_f1_score_rf_tuned)

OOB F1 Score: 0.8534594914251922

confusion_matrix_sklearn(rf_classifier_tuned, X_test,y_test)

rf_classifier_tuned_model_test_perf=model_performance_classification_sklearn(rf_classifier_tuned, X_test, y_test)
print("Testing performance \n",rf_classifier_tuned_model_test_perf)

Testing performance 
    Accuracy  Recall  Precision      F1
0   0.74385 0.87346    0.77266 0.81997

# importance of features in the tree building ( The importance of a feature is computed as the
#(normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance )

print(pd.DataFrame(rf_classifier_tuned.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))

                                   Imp
education_of_employee          0.23283
prevailing_wage                0.19023
no_of_employees                0.15857
yr_of_estab                    0.13617
has_job_experience             0.07910
unit_of_wage_Hour              0.02860
continent_Europe               0.02565
unit_of_wage_Year              0.02159
region_of_employment_Midwest   0.01592
region_of_employment_West      0.01508
continent_Asia                 0.01481
full_time_position             0.01377
region_of_employment_South     0.01353
requires_job_training          0.01349
continent_North America        0.01285
region_of_employment_Northeast 0.01272
continent_South America        0.00618
continent_Africa               0.00274
region_of_employment_Island    0.00262
unit_of_wage_Week              0.00158
continent_Oceania              0.00145
unit_of_wage_Month             0.00053

feature_names1 = X_train.columns
importances = rf_classifier_tuned.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12,12))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [feature_names1[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()

• The tuned Randomed forest model still has good performance but is still overfiiting by 4%.Thus, may not generalize well on unseen data.

Bagging model tuned

# grid search for bagging classifier
bagging_tuned = DecisionTreeClassifier(class_weight={0:0.40,1:0.60},random_state=1)
param_grid = {'base_estimator':[bagging_tuned],
              'n_estimators':[5,7,15,51,101],
              'max_features': [0.7,0.8,0.9,1]
             }
# Define the scoring metric
scorer = metrics.make_scorer(f1_score)

grid = GridSearchCV(BaggingClassifier(random_state=1,bootstrap=True), param_grid=param_grid, scoring = scorer, cv = 5)
grid.fit(X_train, y_train)

## getting the best estimator
bagging_tuned  = grid.best_estimator_
bagging_tuned.fit(X_train,y_train)

BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight={0: 0.4,
                                                                      1: 0.6},
                                                        random_state=1),
                  max_features=0.7, n_estimators=51, random_state=1)

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confusion_matrix_sklearn(bagging_tuned, X_train,y_train)

bagging_classifier_tuned_model_train_perf=model_performance_classification_sklearn(bagging_tuned, X_train,y_train)
print("Training performance \n",bagging_classifier_tuned_model_train_perf)

Training performance 
    Accuracy  Recall  Precision      F1
0   0.99804 0.99983    0.99724 0.99853

# Access the OOB predictions
oob_predictions_bagging_tuned = bagging_tuned.predict(X_train)

# Compute f1-score using OOB predictions and true labels
oob_f1_score_bagging_tuned = f1_score(y_train, oob_predictions_bagging_tuned)

print("OOB F1 Score:", oob_f1_score_bagging_tuned)

OOB F1 Score: 0.9985329253468583

confusion_matrix_sklearn(bagging_tuned, X_test,y_test)

bagging_classifier_tuned_model_test_perf=model_performance_classification_sklearn(bagging_tuned, X_test, y_test)
print("Testing performance \n",bagging_classifier_tuned_model_test_perf)

Testing performance 
    Accuracy  Recall  Precision      F1
0   0.72684 0.88737    0.74963 0.81270

# Access feature importances of the base estimator
if hasattr(bagging_tuned.base_estimator, 'feature_importances_'):
    feature_importances = bagging_tuned.base_estimator.feature_importances_
    print("Feature Importances:", feature_importances)
else:
    print("Base estimator does not have feature importances attribute.")

Base estimator does not have feature importances attribute.

• The tuned Bagging classifier model is still moderately overfiiting, eventhough it still performing well in terms on f1_score.
• However, this model cannot be used be cause it appears to be the worst in terms of generalize well on unseen data.

Decision tree (tuned)

Pre-pruned

# Define the DecisionTreeClassifier with initial hyperparameters
dtree_pre_prune = DecisionTreeClassifier(criterion='gini', random_state=1)

# Define the parameter grid for hyperparameter tuning
param_grid = { 'max_depth': np.arange(2,6),
    'min_samples_leaf': [1, 4, 7],
    'max_leaf_nodes' : [10, 15],
    'min_impurity_decrease': [0.0001,0.001]
}

# Perform Grid Search with Cross-Validation
grid = GridSearchCV(dtree_pre_prune  , param_grid=param_grid, scoring='f1', cv=5)
grid.fit(X_train, y_train)

# Get the best DecisionTreeClassifier estimator
best_dtree_pre_prune_estimator = grid.best_estimator_

# Print the best parameters found by GridSearchCV
print("Best Parameters:", grid.best_params_)

# Fit the best estimator to the training data
dtree_pre_prune = grid.best_estimator_

Best Parameters: {'max_depth': 4, 'max_leaf_nodes': 15, 'min_impurity_decrease': 0.0001, 'min_samples_leaf': 7}

confusion_matrix_sklearn(dtree_pre_prune, X_train,y_train)

dtree_pre_prune_model_train_perf=model_performance_classification_sklearn(dtree_pre_prune, X_train,y_train)
print("Training performance \n",dtree_pre_prune_model_train_perf)

Training performance 
    Accuracy  Recall  Precision      F1
0   0.73817 0.91715    0.74790 0.82392

confusion_matrix_sklearn(dtree_pre_prune, X_test,y_test)

dtree_pre_prune_model_test_perf=model_performance_classification_sklearn(dtree_pre_prune, X_test,y_test)
print("Testing performance \n",dtree_pre_prune_model_test_perf)

Testing performance 
    Accuracy  Recall  Precision      F1
0   0.72985 0.91557    0.74096 0.81907

# importance of features in the tree building ( The importance of a feature is computed as the
#(normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance )

print(pd.DataFrame(dtree_pre_prune.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))

                                   Imp
education_of_employee          0.52238
has_job_experience             0.22124
prevailing_wage                0.10987
continent_Europe               0.07028
unit_of_wage_Hour              0.04934
continent_Asia                 0.01522
region_of_employment_West      0.00700
region_of_employment_Northeast 0.00467
continent_Oceania              0.00000
continent_North America        0.00000
continent_Africa               0.00000
region_of_employment_South     0.00000
region_of_employment_Island    0.00000
region_of_employment_Midwest   0.00000
unit_of_wage_Year              0.00000
unit_of_wage_Week              0.00000
unit_of_wage_Month             0.00000
full_time_position             0.00000
yr_of_estab                    0.00000
no_of_employees                0.00000
requires_job_training          0.00000
continent_South America        0.00000

feature_names5= X_train.columns
importances = dtree_pre_prune.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12,12))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [feature_names5[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()

Post-pruned(with class weight)

# Define the DecisionTreeClassifier with initial hyperparameters
dtree_post_prune = DecisionTreeClassifier(criterion='gini', class_weight={0: 0.40, 1: 0.60}, random_state=1)

# Define the parameter grid for hyperparameter tuning
param_grid = {
    'ccp_alpha': [0.0, 0.01, 0.1, 0.5, 1.0]  # Varying values of ccp_alpha for post-pruning
}

# Perform Grid Search with Cross-Validation
grid = GridSearchCV(dtree_post_prune , param_grid=param_grid, scoring='f1', cv=5)
grid.fit(X_train, y_train)

# Get the best DecisionTreeClassifier estimator
best_dtree_post_prune_estimator = grid.best_estimator_

# Print the best parameters found by GridSearchCV
print("Best Parameters:", grid.best_params_)

# Fit the best estimator to the training data
dtree_post_prune = grid.best_estimator_

Best Parameters: {'ccp_alpha': 0.01}

confusion_matrix_sklearn(dtree_post_prune, X_train,y_train)

dtree_post_prune_model_train_perf=model_performance_classification_sklearn(dtree_post_prune, X_train,y_train)
print("Training performance \n",dtree_post_prune_model_train_perf)

Training performance 
    Accuracy  Recall  Precision      F1
0   0.73032 0.89323    0.75048 0.81565

confusion_matrix_sklearn(dtree_post_prune, X_test,y_test)

dtree_post_prune_model_test_perf=model_performance_classification_sklearn(dtree_post_prune, X_test, y_test)
print("Testing performance \n",dtree_post_prune_model_test_perf)

Testing performance 
    Accuracy  Recall  Precision      F1
0   0.72580 0.89403    0.74587 0.81326

# importance of features in the tree building ( The importance of a feature is computed as the
#(normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance )

print(pd.DataFrame(dtree_post_prune.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))

                                   Imp
education_of_employee          0.66499
unit_of_wage_Hour              0.33501
region_of_employment_Midwest   0.00000
continent_Oceania              0.00000
continent_North America        0.00000
continent_Europe               0.00000
continent_Asia                 0.00000
continent_Africa               0.00000
region_of_employment_West      0.00000
region_of_employment_South     0.00000
region_of_employment_Northeast 0.00000
region_of_employment_Island    0.00000
has_job_experience             0.00000
unit_of_wage_Year              0.00000
unit_of_wage_Week              0.00000
unit_of_wage_Month             0.00000
full_time_position             0.00000
prevailing_wage                0.00000
yr_of_estab                    0.00000
no_of_employees                0.00000
requires_job_training          0.00000
continent_South America        0.00000

feature_names4= X_train.columns
importances = dtree_post_prune.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12,12))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [feature_names4[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()

• The performance for both The post pruned and pre-pruned decision tree has improved when compared to the basic decision tree.
• Both models are also fitting very well,and can generalized on unseen data very well now.

Stacking model

• For the stacking model, I will be using pre-pruned decision tree model,tuned Ada Boost model, and and tuned GBM model as meta estimators.
• The XGB classifier will be used as the final estimator.

estimators=[('Decision Tree pre pruned', dtree_pre_prune),('Ada boosting', ab_tuned),
           ('Gradient boosting', gb_tuned )]
final_estimator=XGBClassifier(random_state=1)

stacking_estimator=StackingClassifier(estimators=estimators, final_estimator=final_estimator,cv=5)
stacking_estimator.fit(X_train,y_train)

StackingClassifier(cv=5,
                   estimators=[('Decision Tree pre pruned',
                                DecisionTreeClassifier(max_depth=4,
                                                       max_leaf_nodes=15,
                                                       min_impurity_decrease=0.0001,
                                                       min_samples_leaf=7,
                                                       random_state=1)),
                               ('Ada boosting',
                                AdaBoostClassifier(algorithm='SAMME',
                                                   base_estimator=DecisionTreeClassifier(max_depth=1,
                                                                                         random_state=1),
                                                   learning_rate=0.8,
                                                   n_estimators=20,
                                                   random_state=1)),
                               ('...
                                                 feature_types=None, gamma=None,
                                                 grow_policy=None,
                                                 importance_type=None,
                                                 interaction_constraints=None,
                                                 learning_rate=None,
                                                 max_bin=None,
                                                 max_cat_threshold=None,
                                                 max_cat_to_onehot=None,
                                                 max_delta_step=None,
                                                 max_depth=None,
                                                 max_leaves=None,
                                                 min_child_weight=None,
                                                 missing=nan,
                                                 monotone_constraints=None,
                                                 multi_strategy=None,
                                                 n_estimators=None, n_jobs=None,
                                                 num_parallel_tree=None,
                                                 random_state=1, ...))

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confusion_matrix_sklearn(stacking_estimator, X_train,y_train)

stacking_estimator_model_train_perf = model_performance_classification_sklearn(stacking_estimator, X_train, y_train)
print("Training performance \n",stacking_estimator_model_train_perf)

Training performance 
    Accuracy  Recall  Precision      F1
0   0.74697 0.86628    0.77946 0.82058

confusion_matrix_sklearn(stacking_estimator, X_test,y_test)

stacking_estimator_model_test_perf = model_performance_classification_sklearn(stacking_estimator, X_test, y_test)
print("Testing performance \n",stacking_estimator_model_test_perf)

Testing performance 
    Accuracy  Recall  Precision      F1
0   0.73927 0.86092    0.77404 0.81517

• The stacking model is also performing well, though there is very little over-fitting with the model.

• The F1_score of this model still slightly falls short when compared to the performance of the tuned GBM model.

Model Performance Comparison and Conclusions

Model comparism

# Training performance comparison

models_train_comp_df = pd.concat([
    dtree_model_train_perf.T, bagging_model_train_perf.T, bagging_wt_model_train_perf.T,
    rf_model_train_perf.T, rf_wt_model_train_perf.T, gb_model_train_perf.T,
    ab_classifier_model_train_perf.T, xgb_classifer_model_train_perf.T,
    gb_model_tuned_train_perf.T, ab_model_tuned_train_perf.T, xgb_tuned_model_train_perf.T,
    rf_classifier_tuned_model_train_perf.T, bagging_classifier_tuned_model_train_perf.T,
    dtree_post_prune_model_train_perf.T, dtree_pre_prune_model_train_perf.T, stacking_estimator_model_train_perf .T,
], axis=1)

models_train_comp_df.columns = [
    "Decision Tree",
    "Bagging classifer",
    "Bagging Estimator wt weights",
    "Random Forest classifier",
    "Random Forest classifier wt weights",
    "Gradient Boost classifier",
    "Adaboost classifier",
    "XGB classifier",
    "Gradient Boost Tuned",
    "Adaboost Tuned",
    "XGB Tuned",
    "Random Forest Classifier tuned",
    "Bagging classifier tuned",
    'Decesion tree classifier(post-pruned)',
    "Decision tree classifier(pre-pruned)",
    "Stacking estimator"
]

print("Training performance comparison:")
models_train_comp_df

Training performance comparison:

	Decision Tree	Bagging classifer	Bagging Estimator wt weights	Random Forest classifier	Random Forest classifier wt weights	Gradient Boost classifier	Adaboost classifier	XGB classifier	Gradient Boost Tuned	Adaboost Tuned	XGB Tuned	Random Forest Classifier tuned	Bagging classifier tuned	Decesion tree classifier(post-pruned)	Decision tree classifier(pre-pruned)	Stacking estimator
Accuracy	1.00000	0.98312	0.98458	1.00000	1.00000	0.75791	0.73710	0.84587	0.74983	0.73688	0.76233	0.79160	0.99804	0.73032	0.73817	0.74697
Recall	1.00000	0.98372	0.98539	1.00000	1.00000	0.88332	0.88945	0.93184	0.89801	0.89499	0.88903	0.90859	0.99983	0.89323	0.91715	0.86628
Precision	1.00000	0.99095	0.99147	1.00000	1.00000	0.78232	0.75859	0.85143	0.76716	0.75596	0.78405	0.80464	0.99724	0.75048	0.74790	0.77946
F1	1.00000	0.98732	0.98842	1.00000	1.00000	0.82976	0.81882	0.88982	0.82744	0.81962	0.83325	0.85346	0.99853	0.81565	0.82392	0.82058

# Test performance comparison

models_test_comp_df = pd.concat([
    dtree_model_test_perf.T, bagging_model_test_perf.T, bagging_wt_model_test_perf.T,
    rf_model_test_perf.T, rf_wt_model_test_perf.T, gb_model_test_perf.T,
    ab_classifier_model_test_perf.T, xgb_classifer_model_test_perf.T,
    gb_model_tuned_test_perf.T, ab_model_tuned_test_perf.T, xgb_tuned_model_test_perf.T,
    rf_classifier_tuned_model_test_perf.T, bagging_classifier_tuned_model_test_perf.T,
    dtree_post_prune_model_test_perf.T, dtree_pre_prune_model_test_perf.T, stacking_estimator_model_test_perf.T
], axis=1)

models_test_comp_df.columns = [
    "Decision Tree",
    "Bagging estimator",
    "Bagging Estimator wt weights",
    "Random Forest classifier",
    "Random Forest classifier wt weights",
    "Gradient Boost classifier",
    "Adaboost classifier",
    "XGB classifier",
    "Gradient Boost Tuned",
    "Adaboost Tuned",
    "XGB Tuned",
    "Random Forest Classifier tuned",
    "Bagging classifier tuned",
    'Decision tree classifier(post-pruned)',
    "Decision tree classifier(pre-pruned)",
    "Stacking Estimator"
]

print("Test performance comparison:")
(models_test_comp_df)

Test performance comparison:

	Decision Tree	Bagging estimator	Bagging Estimator wt weights	Random Forest classifier	Random Forest classifier wt weights	Gradient Boost classifier	Adaboost classifier	XGB classifier	Gradient Boost Tuned	Adaboost Tuned	XGB Tuned	Random Forest Classifier tuned	Bagging classifier tuned	Decision tree classifier(post-pruned)	Decision tree classifier(pre-pruned)	Stacking Estimator
Accuracy	0.66130	0.69780	0.69008	0.72305	0.72083	0.74555	0.73299	0.72828	0.74241	0.73155	0.74451	0.74385	0.72684	0.72580	0.72985	0.73927
Recall	0.74535	0.77160	0.76631	0.83565	0.82997	0.87640	0.88521	0.85524	0.89618	0.89246	0.87796	0.87346	0.88737	0.89403	0.91557	0.86092
Precision	0.74696	0.77494	0.76887	0.76948	0.76994	0.77298	0.75644	0.76543	0.76072	0.75194	0.77116	0.77266	0.74963	0.74587	0.74096	0.77404
F1	0.74615	0.77326	0.76759	0.80120	0.79883	0.82144	0.81578	0.80785	0.82292	0.81619	0.82110	0.81997	0.81270	0.81326	0.81907	0.81517

• The tuned Gradient boost is the best performance with an F1_SCORE of 82.23%. - It also one of the best two models in terms of generalizing on unseen data as it is almost perfectly fitted, the one almost perfectly fitting is the Adaboost tuned.
• The top two models are are GBM tuned and XGB tuned. Though the XGB tuned model is slightly overfited.

• The final model selceted is the GBM tuned model.

Final prediction and conclusion

# importance of features in the tree building ( The importance of a feature is computed as the
#(normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance )

print(pd.DataFrame(gb_tuned.feature_importances_, columns = ["Imp"], index = X_train.columns).sort_values(by = 'Imp', ascending = False))

                                   Imp
education_of_employee          0.50100
has_job_experience             0.16214
prevailing_wage                0.06961
unit_of_wage_Hour              0.06483
continent_Europe               0.05783
region_of_employment_Midwest   0.03336
unit_of_wage_Year              0.02089
region_of_employment_South     0.02024
continent_North America        0.01625
region_of_employment_West      0.01380
continent_Asia                 0.01150
region_of_employment_Northeast 0.00743
yr_of_estab                    0.00649
no_of_employees                0.00514
full_time_position             0.00390
continent_South America        0.00361
requires_job_training          0.00113
continent_Africa               0.00046
region_of_employment_Island    0.00021
unit_of_wage_Month             0.00011
unit_of_wage_Week              0.00007
continent_Oceania              0.00000

feature_names7 = X_train.columns
importances = gb_tuned.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12,12))
plt.title('Feature Importances')
plt.barh(range(len(indices)), importances[indices], color='violet', align='center')
plt.yticks(range(len(indices)), [feature_names7[i] for i in indices])
plt.xlabel('Relative Importance')
plt.show()

• Education level of employee is the most important feature which alligns with my findings during EDA.
• Job Experience is the second most important feature.
• Other than the above two, the other features have very little importance predicting if a employee visa application will be certified or denied.

Actionable Insights and Recommendations

• The final model is quite strong in predicting visa case status going forward as long as the features supplied are the same with what was used in training the model. With this model, visa approval or denial processes can be done with accuracy mimicking the key factors important to immigration staffs when they make visa status decisions. This will also reduce workload and stream-line work flow for immigration officials.

• Contrary to pre-model building believes that prevailing wages play a big role in the immigration official decisions in approval. This model has highlighted that this is not the case. Though, availability of data on the kind of jobs the employees will be doing in the US would have made the immigration official to consider more the prevailing wages when making their decision. For example, for dangerous jobs that require higher pay locally, if foreign employees are paid lower wages, this might lead to such visas been denied.

• Hence, further data on job nature of these employees and the payment on the employees contract need to be collected during visa processing.

• The education level of employee play the biggest role in visa case status decisions.The final model and EDA agree greatly in this observation. The higher the educational level the more likely the Visa will be approved, as those employees with very high educational qualifications are likely possess skills not readily available in country. As observed those with doctorates degrees have an 80% chance of their visa been approved.

• Similiarly, job experience is hugely factored in by immigration officials when processing visas. Their assumption is that experienced employee may possess critical skills in-demand in-country. This has also been verified by analysis of the data using the final model.

• Therefore, the immigration authorities should create a system that prioritizes faster processing of visas for employees with doctorate degrees as well as employees with many years of experience.

• Also,it was also observed that employees with unit wage of hourly are more likely to get their Visas denied. This is understandable because hourly paid jobs make it difficult to know the potential yearly income of the employee. Yearly unit of income is the preffered unit the IRS use for calculating annual income taxes. Also, jobs which require the best brain from around the world are likely to use a yearly unit of wage. The immigration should put a circular to potential employers sponsoring employee visa application. The message should be that, they should endeavour to use yearly unit of wages in theit applications for ease of visa processing.

• From the data, immigration officials are not racially decriminatory when reviewing visa applications, this is demonstated by the quite similiar visa approval rates accross all the continents. This is quite commendable and should be emulated by other immigrations all over the world.